a heuristic approach for the travelling purchaser problem abstract the travelling purchaser problem tpp is a known generalization of the travelling salesman problem and is defined as follows let us consider a set of products and a set of markets each market is provided with a limited amount of each product at a known price the tpp consists in selecting a subset of markets such that a given demand of each product can be purchased minimizing the routing cost and the purchasing cost this problem arises in several applications mainly in routing and scheduling contexts and it is np hard in the strong sense a new heuristic approach based on a local search scheme exploring a new neighbourhood structure is proposed the key idea is to perform a k exchange of markets instead of the classical 1 exchanges a neighbour of a given tpp solution is another tpp solution obtained by removing a path of consecutive markets and by inserting other markets so as to restore the feasibility this proposal is evaluated on a broad class of instances from literature where the routing costs are euclidean distances computational results show that our proposal is favourably compared to previous heuristic algorithms in literature

a fast and effective heuristic for the orienteering problem abstract in the orienteering problem start and end points are specified along with other locations which have associated scores given a fixed amount of time the goal is to determine a path from the start point to the end point through a subset of locations in order to maximize the total path score in this paper a fast and extremely effective heuristic is presented and tested on 67 problems taken from the literature and 40 new test problems the computational results are presented in detail

the multi depot vehicle routing problem with inter depot routes this article addresses an extension of the multi depot vehicle routing problem in which vehicles may be replenished at intermediate depots along their route it proposes a heuristic combining the adaptative memory principle a tabu search method for the solution of subproblems and integer programming tests are conducted on randomly generated instances

an efficient tabu search approach for the 0 1 multidimensional knapsack problem abstract in this paper we describe a new approach to tabu search ts based on strategic oscillation and surrogate constraint information that provides a balance between intensification and diversification strategies new rules needed to control the oscillation process are given for the 0 1 multidimensional knapsack 0 1 mkp based on a portfolio of test problems from the literature our method obtains solutions whose quality is at least as good as the best solutions obtained by previous methods especially with large scale instances these encouraging results confirm the efficiency of the tunneling concept coupled with surrogate information when resource constraints are present

a tssp 1 decomposition strategy for the vehicle routing problem abstract the basic capacity constrained vehicle routing problem vrp is to determine a set of capacity feasible vehicle routes with minimum total travel cost so that all customers are visited by exactly one vehicle in this paper we introduce a new decomposition strategy for the vrp which separates the decisions of a dispatcher from those of the individual drivers the dispatcher is responsible for assigning a reward value to each of the customer cities so that each customer will be visited by exactly one vehicle based on the assigned reward values together with the given travel costs each vehicle driver is responsible for choosing which customers to visit and determining an individual feasible route the driver s problem is modeled as a traveling salesman subset tour problem with one additional constraint tssp 1 the tssp 1 decomposition is based on aagrangian relaxation and is capable of producing valid lower bounds for the vrp the best bound attainable is shown to be at least as good as the bound obtained by solving the linear programming relaxation of the classic set partitioning formulation of the vrp to tet the practical value of the decomposition we present a decomposition based heuristic and examine its performance on a collection of problems from the literature

an efficient composite heuristic for the symmetric generalized traveling salesman problem the main purpose of this paper is to introduce a new composite heuristic for solving the generalized traveling salesman problem the proposed heuristic is composed of three phases the construction of an initial partial solution the insertion of a node from each non visited node subset and a solution improvement phase we show that the heuristic performs very well on 36 tsplib problems which have been solved to optimality by other researchers we also propose some simple heuristics that can be used as basic blocks to construct more efficient composite heuristics

how to be a better bayesian abstract consider an experiment yielding an observable random quantity x whose distribution is indexed by a real parameter o suppose that a statistician is prepared to execute a bayes procedure in estimating o and has quantified available prior information about o in his chosen prior distribution g suppose that before this estimation process is completed the statistician becomes aware of the outcome y of a similar experiment in this article we investigate the questions of whether and when this additional information can be exploited so as to provide a better estimate of o in the current experiment we show that in the traditional empirical bayes framework and in situations involving exponential families conjugate priors and squared error loss the answer is essentially always an explicit bayes empirical bayes beb estimator of o is given that is superior to the original bayes estimator showing that the statistician has in these problems the opportunity to be a better bayesian by combi

algorithms to solve the orienteering problem a comparison abstract the multiobjective vending problem mvp keller 1985 is a generalization of the traveling salesman problem tsp where it is not necessary to visit all nodes in the problem definition a special case of the mvp is the orienteering problem op tsiligirodes 1984 et al the problems are np hard a heuristic was designed to solve the general mvp keller 1985 three other heuristics were designed to solve the op tsiligirides 1984 golden et al 1985 the algorithms underlying these heuristics are outlined their performances are compared against three op problems

polyphase barker sequences up to length 36 originally barker 1953 presented binary sequences for synchronization purposes in telecommunications a sequence is called a generalized barker sequence if the magnitude of all autocorrelation sidelobes is less or equal to one it is shown that uniform complex sequences up to length 36 meeting the barker 1953 condition do exist examples are given for length 32 36 the sequences were obtained with a stochastic optimization algorithm that was run with properly selected starting vectors

simultaneous lotsizing and scheduling by combining local search with dual reoptimization abstract the contribution of this paper is twofold on the one hand the particular problem of integrating lotsizing and scheduling of several products on a single capacitated production line is modelled and solved taking into account sequence dependent setup times thereby continuous lotsizes meeting deterministic dynamic demands are to be determined and scheduled with the objective of minimizing inventory holding costs and sequence dependent setup costs on the other hand a new general algorithmic approach is presented a dual reoptimization algorithm is combined with a local search heuristic for solving a mixed integer programming problem this idea is applied to the above lotsizing and scheduling problem by embedding a dual network flow algorithm into threshold accepting and simulated annealing respectively computational tests show the effectiveness of the new solution method

a guided local search metaheuristic for the team orienteering problem in the team orienteering problem top a set of locations is given each with a score the goal is to determine a fixed number of routes limited in length that visit some locations and maximise the sum of the collected scores this paper describes an algorithm that combines different local search heuristics to solve the top guided local search gls is used to improve two of the proposed heuristics an extra heuristic is added to regularly diversify the search in order to explore more areas of the solution space the algorithm is compared with the best known heuristics of the literature and applied on a large problem set the obtained results are almost of the same quality as the results of these heuristics but the computational time is reduced significantly applying gls to solve the top appears to be a very promising technique furthermore the usefulness of exploring more areas of the solution space is clearly demonstrated

semantic evaluations of noise with tonal components in japan france and germany a cross cultural comparison an evaluation of tonal components in noise using a semantic differential approach yields several perceptual and connotative factors this study investigates the effect of culture on these factors with the aid of equivalent listening tests carried out in japan n 20 france n 23 and germany n 20 the data s equivalence level is determined by a bias analysis this analysis gives insight in the cross cultural validity of the scales used for sound character determination three factors were extracted by factor analysis in all cultural subsamples pleasant metallic and power by employing appropriate target rotations of the factor spaces the rotated factors were compared and they yield high similarities between the different cultural subsamples to check cross cultural differences in means an item bias analysis was conducted the a priori assumption of unbiased scales is rejected the differences obtained are partially linked to bias effects acoustical sound descriptors were additionally tested for

fuzzy rule base generation for classification and its minimization via modified threshold accepting this paper addresses the application of a modified threshold accepting algorithm mta for minimizing the number of rules in a fuzzy rule based classification system while guaranteeing high classification power in terms of computational time required the mta outperforms the ga approaches which are applied to this multi objective combinatorial optimization problem in the literature the number of rules used and the classification power are taken as the objectives the original model of ishibuchi et al ieee trans fuzzy systems 3 1995 260 270 is further modified by employing various aggregators such as the operator compensatory and fuzzy and and a convex combinations of min and max operators in place of product and min operators the performance of the present model is demonstrated in the case of fisher s well known iris data and other data appearing in literature less computational time needed in all cases and better classification rate in testing phase in leave one out technique are important contributions of the present model

natural exponential families with quadratic variance functions statistical theory the normal poisson gamma binomial negative binomial and nefghs distributions are the six univariate natural exponential families nef with quadratic variance functions qvf this sequel to morris 1982 treats certain statistical topics that can be handled within this unified nef qvf formulation including unbiased estimation bhattacharyya and cramer rao lower bounds conditional distributions and moments quadratic regression conjugate prior distributions moments of conjugate priors and posterior distributions empirical bayes and g 2 minimax marginal distributions and their moments parametric empirical bayes and characterizations

a multirate dsp model for estimation of discrete probability density functions the problem of estimating a probability density function pdf from measurements has been widely studied by many researchers even though much work has been done in the area of pdf estimation most of it was focused on the continuous case we propose a new model based approach for modeling and estimating discrete probability density functions or probability mass functions this approach is based on multirate signal processing theory and it has several advantages over the conventional histogram method we illustrate the pdf estimation procedure and analyze the statistical properties of the pdf estimates based on this model a novel scheme is introduced that can be used for estimating the pdf in the presence of noise furthermore the proposed ideas are extended to the more general case of estimating multivariate pdfs finally we also consider practical issues such as optimizing the coefficients of a digital filter which is an integral part of the model this allows us to apply the proposed model to solve real world problems simulation results are given where appropriate in order to demonstrate the ideas

a computational study of smoothing heuristics for the traveling salesman problem abstract over the last five years or so data smoothing has been used to improve the performance of heuristics that solve combinatorial optimization problems data smoothing allows a local search heuristic to escape from a poor local optimum in practice data smoothing has worked well when applied to the traveling salesman problem in this paper we conduct an extensive computational study to test the performance of eight smoothing heuristics for the traveling salesman problem in particular we apply eight smoothing heuristics and standard versions of two opt and three opt to 40 randomly generated euclidean problems and 30 problems taken from a well known library of test problems we compare the solutions generated by the heuristics and provide insight into the behavior of the smoothing heuristics

a threshold accepting heuristic with intense local search for the solution of special instances of the traveling salesman problem in real life scheduling variations of the standard traveling salesman problem are very often encountered the aim of this work is to present a new heuristic method for solving three such special instances with a common approach the proposed algorithm uses a variant of the threshold accepting method enhanced with intense local search while the candidate solutions are produced through an insertion heuristic scheme the main characteristic of the algorithm is that it does not require modifications and parameter tuning in order to cope with the three different problems computational results on a variety of real life and artificial problems are presented at the end of this work and prove the efficiency and the ascendancy of the proposed method over other algorithms found in the literature

record breaking optimization results using the ruin and recreate principle a new optimization principle is presented solutions of problems are partly but significantly ruined and rebuilt or recreated afterwards performing this type of change frequently one can obtain astounding results for classical optimization problems the new method is particularly suited for more complex optimization problems discontinuous ones problems with hard to find admissible solutions problems with complex objectives or many constraints the method is an all purpose heuristic numerical results are given for the traveling salesman problem for the vehicle routing problem with time windows and for network optimization numerical evidence for the quality of the proposed principle is given for most of the instances of a research library of problems the ruin and recreate r r implementation achieved the best published results for many instances better or much better solutions could be found

perceptual space pleasantness and periodicity of multi tone sounds technical sounds often contain several tonal components forming a multi tone sound the present study investigates the perception of multi tone sounds consisting of two harmonic complexes with different fundamental frequencies and combination tones with frequencies that are equal to the sum of multiple integers of the two fundamentals the experimental parameter is the ratio between the two fundamental frequencies a total of 15 synthetic multi tone sounds are rated by 37 participants in the first experiment the perceptual space is assessed based on 16 adjective scales using categorical scaling the resulting perceptual space has the four dimensions i pleasant ii power iii temporal structure and iv spectral content of the sounds in the second experiment the pleasantness is measured with a paired comparison test the data consistently show that sounds based on ratios of small integers e g 4 3 are significantly less pleasant than sounds with ratios based on large integers which were constructed by a slight detuning from a ratio of small integers the repetition rate derived from an autocorrelation analysis of the stimuli turns out to be a good predictor of the un pleasantness sensation

search space smoothing for combinatorial optimization problems commonly there are two types of local search approaches known to treat combinatorial optimization problems with very complex search space structure one is to introduce very complicated types of local move classes allowing a bypass of high energetic barriers separating different minima the second is introducing a control parameter i e temperature in physics terminology dependent state space walker which is depending on this control parameter more or less easily able to climb over barriers a third less well known but very obvious approach is to smooth the search space i e to eliminate barriers between low energy configurations and therefore to allow a fast and easy approach to the global optimum this procedure will be discussed in depth in the following work

strong linear programming relaxations for the orienteering problem abstract consider a set of nodes each with an associated profit and a set of arcs each with an associated length the objective of the orienteering problem is to find the path beginning at a specified origin and terminating at a specified destination that maximizes total profit subject to 1 a constraint on the length of the path and 2 the condition that no node is visited more than once the problem may be formulated as a 0 1 integer program and it has been shown to be np hard prior research has focused on heuristics that obtain lower bounds on the optimal objective function value some recent research has proposed a branch and bound algorithm that solves a linear programming lp relaxation of the 0 1 model at each node and obtains an optimal orienteering path our research is concerned with tightening the lp relaxation by adding constraints and valid inequalities we propose a procedure to obtain upper bounds by solving three successive linear programs we test our procedure on datasets in existing literature and demonstrate that the average deviation between our upper bounds and the best known lower bounds feasible solutions is less than five percent the quality of upper bound obtained is significantly enhanced over the bound obtained from solving the basic lp relaxation

column generation based heuristics for a generalized location routing problem with profits arising in space exploration abstract the generalized location routing problem with profits is a new routing problem class that combines the vehicle routing problem with profits depot or base selection and the notion of strategies and route tactics to be employed at selected bases the problem simultaneously determines base locations strategies to use at the bases sites to visit and routes to carry out the visits to maximize the sum of profits that can be obtained by visiting sites under resource consumption constraints the problem arises in exploration of planetary bodies where strategies correspond to different technologies a description of the generalized location routing problem with profits and its mathematical formulation as an integer program are provided two solution methodologies to solve the problem branch and price and a three phase heuristic method combined with a generalized randomized adaptive search procedure are proposed numerical experiments for the two solution methodologies are carried out their performance in terms of computation time and optimality gap are analyzed and compared to establish the problem characteristics indicating which method is more advantageous

an effective pso inspired algorithm for the team orienteering problem abstract the team orienteering problem top is a particular vehicle routing problem in which the aim is to maximize the profit gained from visiting customers without exceeding a travel cost time limit this paper proposes a new and fast evaluation process for top based on an interval graph model and a particle swarm optimization inspired algorithm psoia to solve the problem experiments conducted on the standard benchmark of top clearly show that our algorithm outperforms the existing solving methods psoia reached a relative error of 0 0005 whereas the best known relative error in the literature is 0 0394 our algorithm detects all but one of the best known solutions moreover a strict improvement was found for one instance of the benchmark and a new set of larger instances was introduced

multitone signals with low crest factor after an introductory discussion of real valued and complex signals it is shown that minimizing the crest factor cf of multitone signals is closely related to the construction of complex sequences with low sidelobes in their aperiodic autocorrelation function inspired by this observation a lower bound on the achievable cf is derived four differing algorithms for the reduction of the cf of complex multitone signals are compared with each other by computer simulation the preferred algorithm is presented in detail and its convergence is proven examples of multitone signals with up to 15 tones and lower cf than previously reported in the literature are given

a variable neighborhood search heuristic for periodic routing problems the aim of this paper is to propose a new heuristic for the periodic vehicle routing problem pvrp without time windows the pvrp extends the classical vehicle routing problem vrp to a planning horizon of several days each customer requires a certain number of visits within this time horizon while there is some flexibility on the exact days of the visits hence one has to choose the visit days for each customer and to solve a vrp for each day our method is based on variable neighborhood search vns computational results are presented that show that our approach is competitive and even outperforms existing solution procedures proposed in the literature also considered is the special case of a single vehicle i e the periodic traveling salesman problem ptsp it is shown that slight changes of the proposed vns procedure is also competitive for the ptsp

minimum directed 1 subtree relaxation for score orienteering problem score orienteering is a variant of the orienteering game where competitors start at a specified control point visit as many control points as possible and thereby collect prizes and return to the starting point within a prescribed amount of time the competitor with the highest amount of prizes wins the game in this paper we propose the minimum directed 1 subtree problem as a new relaxation to this problem and develop two methods to improve its lower bound we describe a cut and dual simplex method and a lagrangian relaxation method and construct an algorithm that combines these two methods in an appropriate way computational experiments are carried out to analyse the behavior of the proposed relaxation with respect to the characteristics of the test problems especially for large scale instances we find that the proposed relaxation is superior to the ordinary assignment relaxation

new optimization heuristics in a former paper we introduced a very effective new general purpose optimization principle we compared this method which we called threshold accepting ta with the well known simulated annealing sa method for discrete optimization the empirical results demonstrated the superiority of the ta algorithm in further experiments with the ta principle we discovered two new powerful optimization heuristics the great deluge algorithm gda and the record to record travel rrt these algorithms resemble in their structure the formerly studied ta and the sa method the differences lie in their acceptance rules for worse intermediate solutions both the gda and the rrt are essentially one parameter algorithms i e for the achievement of best possible performance a good choice is necessary only for a single parameter this is in contrast for instance to the classical as algorithm where it is necessary to choose carefully a certain sequence of parameters the so called annealing schedule the quality of the computational results obtained so far by rrt and gda shows that the new algorithms behave equally well as ta and thus a fortiori better than sa

loudness annoyance and noisiness produced by single tone noise complexes single tones centered within the noise spectrum were added to three different broadband spectra flat low pass and high pass judgments of overall loudness annoyance and noisiness perceived magnitude were obtained by absolute magnitude estimation ame supplemented by loudness matching the data were evaluated to determine how the overall spl of the noise tone complex and tone to noise ratio affect judged perceived magnitude in addition the relationship among the three judged attributes was assessed results obtained with the different noise spectra show that the growth of perceived magnitude is a nonmonotonic function of the overall spl of the noise tone complex more summation between tone and noise was found for relatively small tone to noise ratios 5 10 15 db as measured in 1 3 octave bands than for relatively large tone to noise ratios 20 db and greater data analysis suggests that the extent of the increments and decrements in perceived magnitude depends on the absolute loudnesses of the component stimuli the interaction between a specific tone frequency and noise spectrum and the attribute judged an attempt is made to quantify the observed effects and to relate them to the published results of other investigators

estimation of the shape parameter k of the negative binomial distribution using approximate bayesian methods an estimator of the shape parameter of the negative binomial distribution is introduced most of the available estimators of this parameter such as the method of moment and the maximum likelihood estimators mme and mle give inadmissible values when the mean of the observed sample is larger than its variance in this case the mme gives values while the mle is infinite such observed sample in not unlikely when the mean of the population is close to its variance the suggested estimator always has valid values and turned out to be more efficient for most values of the parameter space family size data is used for illustration of the procedure

general local search methods abstract this paper is a tutorial introduction to three recent yet widely used general heuristics simulated annealing tabu search and genetic algorithms a relatively precise description and an example of application are provided for each of the methods as well as a tentative evaluation and comparison from a pragmatic point of view

the team orienteering problem with time windows an lp based granular variable neighborhood search abstract the team orienteering problem top is a known np hard problem that typically arises in vehicle routing and production scheduling contexts in this paper we introduce a new solution method to solve the top with hard time window constraints toptw we propose a variable neighborhood search vns procedure based on the idea of exploring most of the time granular instead of complete neighborhoods in order to improve the algorithm s efficiency without loosing effectiveness the method provides a general way to deal with granularity for those routing problems based on profits and complicated by time constraints extensive computational results are reported on standard benchmark instances performance of the proposed algorithm is compared to optimal solution values when available or to best known solution values obtained by state of the art algorithms the method comes out to be on average quite effective allowing to improve the best know values for 25 test instances

a tabu search heuristic for the undirected selective travelling salesman problem the undirected selective travelling salesman problem stsp is defined on a graph g v e with positive profits associated with vertices and distances associated with edges the stsp consists of determining a maximal profit hamiltonian cycle over a subset of v whose length does not exceed a preset limit l we describe a tabu search ts heuristic for this problem the algorithm iteratively inserts clusters of vertices in the current tour or removes a chain of vertices tests performed on randomly generated instances with up to 300 vertices show that the algorithm consistently yields near optimal solutions

applying simulated annealing using different methods for the neighborhood search in forest planning problems adjacency constraints along with even flow harvest constraints are important in long term forest planning simulated annealing sa is previously successfully applied when addressing such constraints the objective of this paper was to assess the performance of sa under three new methods of introducing biased probabilities in the management unit mu selection and compare them to the conventional method that assumes uniform probabilities the new methods were implemented as a search vector approach based on the number of treatment schedules describing sequences of silvicultural treatments over time and standard deviation of net present value within mus methods 2 and 3 respectively and by combining the two approaches method 4 we constructed three hundred hypothetical forests datasets for three different landscapes characterized by different initial age class distributions young normal and old each dataset encompassed 1600 management units the evaluation of the methods was done by means of objective function values first feasible iteration and time consumption introducing a bias in the mu selection improves solutions compared to the conventional method method 1 however an increase of computational time is in general needed for the new methods method 4 is the best alternative because for large parts of the datasets produced the best average and maximum objective function values and had lower time consumption than methods 2 and 3 although method 4 performed very well methods 2 and 3 should not be neglected because for a considerable number of datasets the maximum objective function values were obtained by these methods

an exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles the vehicle routing problem with multiple use of vehicles is a variant of the classical vehicle routing problem it arises when each vehicle performs several routes during the workday due to strict time limits on route duration e g when perishable goods are transported the routes are defined over customers with a revenue a demand and a time window given a fixed size fleet of vehicles it might not be possible to serve all customers thus the customers must be chosen based on their associated revenue minus the traveling cost to reach them we introduce a branch and price approach to address this problem where lower bounds are computed by solving the linear programming relaxation of a set packing formulation using column generation the pricing subproblems are elementary shortest path problems with resource constraints computational results are reported on euclidean problems derived from well known benchmark instances for the vehicle routing problem with time windows

adaptive demixing in poisson mixture models let x 1 x 2 x n be an i i d sample from the poisson mixture distribution p x 1 x 0 s x e s f s ds rates of convergence in mean integrated squared error mise of orthogonal series estimators for the mixing density f supported on a b are studied for the holder class of densities whose r th derivative is lipschitz a the mise converges at the rate log n log log n 2 r for sobolev classes of densities whose r th derivative is square integrable the mise converges at the rate log n log log n 2r the estimator is adaptive over both these classes for the sobolev class a lower bound on the minimax rate of convergence is log n log log n 2r and so the orthogonal polynomial estimator is rate optimal

the late acceptance hill climbing heuristic abstract this paper introduces a new and very simple search methodology called late acceptance hill climbing lahc it is a local search algorithm which accepts non improving moves when a candidate cost function is better than it was a number of iterations before this number appears as a single algorithmic input parameter which determines the total processing time of the search procedure the properties of this method are experimentally studied in this paper with a range of travelling salesman and exam timetabling benchmark problems also we present a fair comparison of lahc with well known search techniques which employ a cooling schedule simulated annealing sa threshold accepting ta and the great deluge algorithm gda in addition we discuss the success of the method in winning an international competition to automatically solve the magic square problem our experiments have shown that the lahc approach is simple easy to implement and yet is an effective search procedure for most of the studied instances especially for the large sized ones its average performance is better than competitor methods moreover the lahc approach has an additional advantage in contrast to the above cooling schedule based methods in its scale independence we present an example where the rescaling of a cost function in the travelling salesman problem dramatically deteriorates the performance of three cooling schedule based techniques but has absolutely no influence upon the performance of lahc

influence of rounding errors on the quality of heuristic optimization algorithms abstract search space smoothing and related heuristic optimization algorithms provide an alternative approach to simulated annealing and its variants while simulated annealing traverses barriers in the energy landscape at finite temperatures search space smoothing intends to remove these barriers so that a greedy algorithm is sufficient to find the global minimum several formulas for smoothing the energy landscape have already been applied one of them making use of the finite numerical precision on a computer in this paper we thoroughly investigate the effect of finite numerical accuracy on the quality of results achieved with heuristic optimization algorithms we present computational results for the traveling salesman problem