the influence of stimulus envelope and fine structure on the binaural masking level difference a masking level difference mld paradigm was used to investigate the influence of stimulus envelope and stimulus fine structure characteristics on monaural and binaural hearing the degree of masker envelope fluctuation was manipulated by selecting narrow band noises 50 hz on a continuum of values of the normalized fourth moment of the envelope the noises were specified as low noise noise lnn medium noise noise mnn and high noise noise hnn fine structure cues were studied by measuring thresholds at 500 and 4000 hz regions in which the availability of such cues to the auditory system differ substantially in addition thresholds were measured for gaussian noise maskers gn and for maskers having a flat magnitude spectrum termed equal magnitude noise emn maskers the results indicated lower noso thresholds for lnn than for the other four masker types furthermore there were no differences in threshold for maskers having moderate and high degrees of envelope fluctuation mnn and hnn the

a new meta heuristic algorithm for continuous engineering optimization harmony search theory and practice most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point these algorithms however reveal a limited approach to complicated real world optimization problems if there is more than one local optimum in the problem the result may depend on the selection of an initial point and the obtained optimal solution may not necessarily be the global optimum this paper describes a new harmony search hs meta heuristic algorithm based approach for engineering optimization problems with continuous design variables this recently developed hs algorithm is conceptualized using the musical process of searching for a perfect state of harmony it uses a stochastic random search instead of a gradient search so that derivative information is unnecessary various engineering optimization problems including mathematical function minimization and structural engineering optimization problems are presented to demonstrate the effectiveness and robustness of the hs algorithm the results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms 2004 elsevier b v all rights reserved

a hybrid simplex search and particle swarm optimization for unconstrained optimization this paper proposes the hybrid nm pso algorithm based on the nelder mead nm simplex search method and particle swarm optimization pso for unconstrained optimization nm pso is very easy to implement in practice since it does not require gradient computation the modification of both the nelder mead simplex search method and particle swarm optimization intends to produce faster and more accurate convergence the main purpose of the paper is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy in a suite of 20 test function problems taken from the literature computational results via a comprehensive experimental study preceded by the investigation of parameter selection show that the hybrid nm pso approach outperforms other three relevant search techniques i e the original nm simplex search method the original pso and the guaranteed convergence particle swarm optimization gcpso in terms of solution quality and convergence rate in a later part of the comparative experiment the nm pso algorithm is compared to various most up to date cooperative pso cpso procedures appearing in the literature the comparison report still largely favors the nm pso algorithm in the performance of accuracy robustness and function evaluation as evidenced by the overall assessment based on two kinds of computational experience the new algorithm has demonstrated to be extremely effective and efficient at locating best practice optimal solutions for unconstrained optimization

the effects of randomizing values of interaural disparities on binaural detection and on discrimination of interaural correlation the purpose of this study was to assess whether randomizing roving baseline interaural parameters of binaural stimuli would adversely affect performance in masking level difference and in interaural correlation discrimination paradigms listeners detection thresholds were measured in noso and nos configurations for both broadband 100 3000 hz and narrow band 450 550 hz maskers centered at 500 hz in addition thresholds of interaural decorrelation from a reference correlation of 1 0 were measured for 100 hz wide bands of noise centered at 500 hz data were obtained under conditions in which either values of itd or values of iid were roved both within and across trials data were also collected in the absence of a rove the range of the rove was 500 s for itds and was 6 db for iids the duration of the stimuli was varied between 17 and 310 ms overall the results indicate that independent of duration roving the interaural cues produced small degradations of performance as compared to data ob

an effective co evolutionary differential evolution for constrained optimization many practical problems can be formulated as constrained optimization problems due to the simple concept and easy implementation the penalty function method has been one of the most common techniques to handle constraints however the performance of this technique greatly relies on the setting of penalty factors which are usually determined by manual trial and error and the suitable penalty factors are often problem dependent and difficult to set in this paper a differential evolution approach based on a co evolution mechanism named cde is proposed to solve the constrained problems first a special penalty function is designed to handle the constraints second a co evolution model is presented and differential evolution de is employed to perform evolutionary search in spaces of both solutions and penalty factors thus the solutions and penalty factors evolve interactively and self adaptively and both the satisfactory solutions and suitable penalty factors can be obtained simultaneously simulation results based on several benchmark functions and three well known constrained design problems as well as comparisons with some existed methods demonstrate the effectiveness efficiency and robustness of the proposed method

teaching learning based optimization with dynamic group strategy for global optimization abstract global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence in recent years there have been some significant developments in these areas regarding the solution of global optimization problems in this paper we propose an improved teaching learning based optimization tlbo algorithm with dynamic group strategy dgs for global optimization problems different to the original tlbo algorithm dgstlbo enables each learner to learn from the mean of his corresponding group rather than the mean of the class in the teacher phase furthermore each learner employs the random learning strategy or the quantum behaved learning strategy in his corresponding group in the learner phase regrouping occurs dynamically after a certain number of generations helping to maintain the diversity of the population and discourage premature convergence to verify the feasibility and effectiveness of the proposed algorithm experiments are conducted on 18 numerical benchmark functions in 10 30 and 50 dimensions the results show that the proposed dgstlbo algorithm is an effective method for global optimization problems

an efficient constraint handling method for genetic algorithms many real world search and optimization problems involve inequality and or equality constraints and are thus posed as constrained optimization problems in trying to solve constrained optimization problems using genetic algorithms gas or classical optimization methods penalty function methods have been the most popular approach because of their simplicity and ease of implementation however since the penalty function approach is generic and applicable to any type of constraint linear or nonlinear their performance is not always satisfactory thus researchers have developed sophisticated penalty functions specific to the problem at hand and the search algorithm used for optimization however the most difficult aspect of the penalty function approach is to find appropriate penalty parameters needed to guide the search towards the constrained optimum in this paper ga s population based approach and ability to make pair wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter careful comparisons among feasible and infeasible solutions are made so as to provide a search direction towards the feasible region once sufficient feasible solutions are found a niching method along with a controlled mutation operator is used to maintain diversity among feasible solutions this allows a real parameter ga s crossover operator to continuously find better feasible solutions gradually leading the search near the true optimum solution gas with this constraint handling approach have been tested on nine problems commonly used in the literature including an engineering design problem in all cases the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier

how novel is the novel black hole optimization approach due to abundance of novel optimization algorithms in recent years the problem of large similarities among methods that are named differently is becoming troublesome and general the question arises if the novel source of inspiration is sufficient to breed an optimization algorithm with a novel name even if its search properties are almost the same as or are even a simplified variant of the search properties of an older and well known method in this paper it is rigidly shown that the recently proposed heuristic approach called the black hole optimization is in fact a simplified version of particle swarm optimization with inertia weight additionally because a large number of metaheuristics developed during the last decade is claimed to be nature inspired a short discussion on inspirations of optimization algorithms is presented

applications of mapping cones over clements lindstrom rings abstract we prove that gotzmann s persistence theorem holds over every clements lindstrom ring we also construct the infinite minimal free resolution of a square free borel ideal over such a ring

binaural detection with narrowband and wideband reproducible noise maskers iv models using interaural time level and envelope differences the addition of out of phase tones to in phase noises results in dynamic interaural level difference ild and interaural time difference itd cues for the dichotic tone in noise detection task several models have been used to predict listeners detection performance based on ild itd or different combinations of the two cues the models can be tested using detection performance from an ensemble of reproducible noise maskers previous models cannot predict listeners detection performance for reproducible noise maskers without fitting the data here two models were tested for narrowband and wideband reproducible noise experiments one model was a linear combination of ild and itd that included the generally ignored correlation between the two cues the other model was based on a newly proposed cue the slope of the interaural envelope difference sied predictions from both models explained a significant portion of listeners performance for detection of a 500 hz tone in wideband noise predictions based on the sied approached the predictable variance in the wideband condition the sied represented a nonlinear combination of ild and itd with the latter cue dominating listeners did not use a common strategy cue to detect tones in the narrowband condition and may use different single frequencies or different combinations of frequency channels

an improved harmony search algorithm for solving optimization problems this paper develops an improved harmony search ihs algorithm for solving optimization problems ihs employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search hs algorithm in this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented the ihs algorithm has been successfully applied to various benchmarking and standard engineering optimization problems numerical results reveal that the proposed algorithm can find better solutions when compared to hs and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems

probability distributions of interaural phase and level differences in binaural detection stimuli probability distributions of interaural phase and level differences are derived for the stimuli used in many studies of binaural detection and interaural correlation discrimination example distributions are shown and summary plots give the standard deviations of these distributions as functions of signal to noise ratio

enhancing the search ability of differential evolution through orthogonal crossover differential evolution de is a class of simple yet powerful evolutionary algorithms for global numerical optimization binomial crossover and exponential crossover are two commonly used crossover operators in current popular de it is noteworthy that these two operators can only generate a vertex of a hyper rectangle defined by the mutant and target vectors therefore the search ability of de may be limited orthogonal crossover ox operators which are based on orthogonal design can make a systematic and rational search in a region defined by the parent solutions in this paper we have suggested a framework for using an ox in de variants and proposed oxde a combination of de rand 1 bin and ox extensive experiments have been carried out to study oxde and to demonstrate that our framework can also be used for improving the performance of other de variants

harmony search algorithm with dynamic control parameters abstract harmony search hs is a population based meta heuristic imitating the music improvisation process which has been successfully applied to optimization problems in recent years this paper presents an effective harmony search algorithm for solving global continuous optimization problems the proposed method presents a novel improvisation process which is different from the classical hs in two aspects firstly the candidate harmony is chosen from the harmony memory by a tournament selection rule so that the harmonies with better fitness will have more opportunities to be used in generating new harmonies secondly two key control parameters pitch adjustment rate par and bandwidth distance bw are adjusted dynamically with respect to the evolution of the search process and the different search spaces of the optimization problems numerical results demonstrate that the proposed algorithm performs much better than the existing hs variants in terms of the solution quality and the stability

constrained optimization based on modified differential evolution algorithm this paper presents a novel constrained optimization based on modified differential evolution algorithm comde in the new algorithm a new directed mutation rule based on the weighted difference vector between the best and the worst individuals at a particular generation is introduced the new directed mutation rule is combined with the modified basic mutation strategy de rand 1 bin where only one of the two mutation rules is applied with the probability of 0 5 the proposed mutation rule is shown to enhance the local search ability of the basic differential evolution de and to get a better trade off between convergence rate and robustness two new scaling factors are introduced as uniform random variables to improve the diversity of the population and to bias the search direction additionally a dynamic non linear increased crossover probability is utilized to balance the global exploration and local exploitation comde also includes a modified constraint handling technique based on feasibility and the sum of constraints violations a new dynamic tolerance technique to handle equality constraints is also adopted the effectiveness and benefits of the new directed mutation strategy and modified basic strategy used in comde has been experimentally investigated the effect of the parameters of the crossover probability function and the parameters of the dynamic tolerance equation on the performance of comde have been analyzed and evaluated by different experiments numerical experiments on 13 well known benchmark test functions and five engineering design problems have shown that the new approach is efficient effective and robust the comparison results between the comde and the other 28 state of the art evolutionary algorithms indicate that the proposed comde algorithm is competitive with and in some cases superior to other existing algorithms in terms of the quality efficiency convergence rate and robustness of the final solution

a note on multi objective improved teaching learning based optimization algorithm mo itlbo recently multi objective improved teaching learning based optimization algorithm mo itlbo has been proposed to solve complex multi objective optimization problems and has been shown to be competitive against various other state of the art algorithms the algorithm was demonstrated on the constrained and unconstrained optimization problems of cec 2009 and was reported to have shown impressive results however some critical steps in the algorithm have not been adequately described and these have become major impediments even for the implementation of mo itlbo in this note we have explained all such issues which need to be convincingly addressed so that independent researchers could evaluate and use mo itlbo for various other applications also two variants of mo itlbo have been suggested whose results enforce that the issues reported in this article are critical to harness the reported benefits of the mo itlbo

adaptive memetic differential evolution with global and local neighborhood based mutation operators differential evolution de is one of the most popular optimization methods for real valued problems and a large number of its variants have been proposed so far however bringing together different ideas that already led to successful de versions is rare in the literature in the present paper a novel de algorithm is proposed in which three among the most efficient concepts already applied separately within de framework are gathered together namely 1 the adaptation of algorithm control parameters and probabilities of using different mutation strategies 2 the use of nelder mead algorithm as a local search method hybridized with de and 3 the splitting mutation into global and local models when local mutation model is based on the concept of neighborhood of individuals organized on a ring topology the performance of the novel algorithm called adaptive memetic de with global and local neighborhood based mutation operators is compared with 13 different de variants on a set of 25 popular problems which include rotated shifted and hybrid composition functions it is found that although none de algorithm outperforms all the others for the majority of problems on average the proposed approach perform better than all 13 de algorithms selected for comparison the proposed algorithm is another heuristic approach developed to solve optimization problems the question may arise whether proposing novel methods is useful as no free lunch theorems for optimization state that the expected performance of all possible heuristics on all possible problems is equal in the last section of the paper the limitations and implications of no free lunch theorems are discussed based on rich but unfortunately frequently neglected literature a very simple continuous and differentiable minimization problem is proposed for which it is empirically verified that each among considered 14 de algorithms perform poorer than random sampling it is also empirically shown that when all considered de algorithms search for the maximum of the proposed problem they found lower minimum than de algorithms searching for the minimum such result is not unexpected according to no free lunch theorems and should be considered as a precaution from generalization of good performance of heuristic optimization methods

hilbert schemes and maximal betti numbers over veronese rings macaulay s theorem macaulay in proc lond math soc 26 531 555 1927 characterizes the hilbert functions of graded ideals in a polynomial ring over a field we characterize the hilbert functions of graded ideals in a veronese ring r the coordinate ring of a veronese embedding of pr 1 we also prove that the hilbert scheme which parametrizes all graded ideals in r with a fixed hilbert function is connected this is an analogue of hartshorne s theorem hartshorne in math ihes 29 5 48 1966 that hilbert schemes over a polynomial ring are connected furthermore we prove that each lex ideal in r has the greatest betti numbers among all graded ideals with the same hilbert function

global best harmony search harmony search hs is a new meta heuristic optimization method imitating the music improvisation process where musicians improvise their instruments pitches searching for a perfect state of harmony a new variant of hs called global best harmony search ghs is proposed in this paper where concepts from swarm intelligence are borrowed to enhance the performance of hs the performance of the ghs is investigated and compared with hs and a recently developed variation of hs the experiments conducted show that the ghs generally outperformed the other approaches when applied to ten benchmark problems the effect of noise on the performance of the three hs variants is investigated and a scalability study is conducted the effect of the ghs parameters is analyzed finally the three hs variants are compared on several integer programming test problems the results show that the three approaches seem to be an efficient alternative for solving integer programming problems 2007 elsevier inc all rights reserved

a self adaptive global best harmony search algorithm for continuous optimization problems this paper presents a self adaptive global best harmony search sghs algorithm for solving continuous optimization problems in the proposed sghs algorithm a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies the harmony memory consideration rate hmcr and pitch adjustment rate par are dynamically adapted by the learning mechanisms proposed the distance bandwidth bw is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature the computational results show that the proposed sghs algorithm is more effective in finding better solutions than the state of the art harmony search hs variants

teaching learning based optimization with global crossover for global optimization problems teaching learning based optimization tlbo is a newly developed population based meta heuristic algorithm it has better global searching capability but it also easily got stuck on local optima when solving global optimization problems this paper develops a new variant of tlbo called teaching learning based optimization with global crossover tlbo gc for improving the performance of tlbo in teaching phase a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima and a new global crossover strategy is incorporated into the learning phase which aims at balancing local and global searching effectively the performance of tlbo gc is assessed by solving global optimization functions with different characteristics compared to the tlbo several modified tlbos and other promising heuristic methods numerical results reveal that the tlbo gc has better optimization performance

binaural processing model based on contralateral inhibition iii dependence on temporal parameters this paper and two accompanying papers breebaart et al j acoust soc am 110 1074 1088 2001 110 1089 1104 2001 describe a computational model for the signal processing of the binaural auditory system the model consists of several stages of monaural and binaural preprocessing combined with an optimal detector simulations of binaural masking experiments were performed as a function of temporal stimulus parameters and compared to psychophysical data adapted from literature for this purpose the model was used as an artificial observer in a three interval forced choice procedure all model parameters were kept constant for all simulations model predictions were obtained as a function of the interaural correlation of a masking noise and as a function of both masker and signal duration furthermore maskers with a time varying interaural correlation were used predictions were also obtained for stimuli with time varying interaural time or intensity differences finally binaural forward maskin

resolutions of a stable ideals abstract let a a 1 a 2 a n be a sequence of integers or we introduce a stable ideals in a polynomial ring and study their homological properties our results generalize results on square free monomial ideals by aramova avramov herzog hibi and srinivasan

a note on teaching learning based optimization algorithm teaching learning based optimization tlbo seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances it is reported that it outperforms some of the well known metaheuristics regarding constrained benchmark functions constrained mechanical design and continuous non linear numerical optimization problems such a breakthrough has steered us towards investigating the secrets of tlbo s dominance this paper reports our findings on tlbo qualitatively and quantitatively through code reviews and experiments respectively our findings have revealed three important mistakes regarding tlbo 1 at least one unreported but important step 2 incorrect formulae on a number of fitness function evaluations and 3 misconceptions about parameter less control additionally unfair experimental settings conditions were used to conduct experimental comparisons e g different stopping criteria the experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy the ultimate goal of this paper is to provide reminders for metaheuristics researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects and to allow fair comparisons of the tlbo algorithm to be made with other metaheuristic algorithms

accounting for binaural detection as a function of masker interaural correlation effects of center frequency and bandwidth binaural detection was measured as a function of the center frequency bandwidth and interaural correlation of masking noise thresholds were obtained for 500 hz or 125 hz s tonal signals and for the latter stimuli noise or signal plus noise transposed to 4 khz a primary goal was assessment of the generality of van der heijden and trahiotis j acoust soc am 101 1019 1022 1997 hypothesis that thresholds could be accounted for by the additive masking effects of the underlying no and n components of a masker having an interaural correlation of results indicated that 1 the overall patterning of the data depended neither upon center frequency nor whether information was conveyed via the waveform or by its envelope 2 thresholds for transposed stimuli improved relative to their low frequency counterparts as bandwidth of the masker was increased 3 the additivity approach accounted well for the data across stimulus conditions but consistently overestimated mlds especially for narrowband maskers 4 a quantitative approach explicitly taking into account the distributions of time varying itd based lateral positions produced by masker alone and signal plus masker waveforms proved more successful albeit while employing a larger set of assumptions parameters and computational complexity

the contribution of static and dynamically varying itds and iids to binaural detection this paper investigates the relative contribution of various interaural cues to binaural unmasking in conditions with an interaurally in phase masker and an out of phase signal mos pi by using a modified version of multiplied noise as the masker and a sinusoid as the signal conditions with only interaural intensity differences iids only interaural time differences itds or combinations of the two were realized in addition the experimental procedure allowed the presentation of specific combinations of static and dynamically varying interaural differences in these conditions with multiplied noise as masker the interaural differences have a bimodal distribution with a minimum at zero iid or itd additionally by using the sinusoid as masker and the multiplied noise as signal a unimodal distribution of the interaural differences was realized through this variation in the shape of the distributions the close correspondence between the change in the interaural cross correlation and the size of the interaural differences is no longer found in contrast to the situation for a gaussian noise masker domnitz and colburn j acoust soc am 59 598 601 1976 when analyzing the mean thresholds across subjects the experimental results could not be predicted from parameters of the distributions of the interaural differences the mean the standard deviation or the root mean square value a better description of the subjects performance was given by the change in the interaural correlation but this measure failed in conditions which produced a static interaural intensity difference the data could best be described by using the energy of the difference signal as the decision variable an approach similar to that of the equalization and cancellation model

cultured differential evolution for constrained optimization a cultural algorithm with a differential evolution population is proposed in this paper this cultural algorithm uses different knowledge sources to influence the variation operator of the differential evolution algorithm in order to reduce the number of fitness function evaluations required to obtain competitive results comparisons are provided with respect to three techniques that are representative of the state of the art in the area the results obtained by our algorithm are similar in quality to those obtained by the other approaches with respect to which it was compared however our approach requires a lower number of fitness function evaluations than the others

niching particle swarm optimization with local search for multi modal optimization multimodal optimization is still one of the most challenging tasks for evolutionary computation in recent years many evolutionary multi modal optimization algorithms have been developed all these algorithms must tackle two issues in order to successfully solve a multi modal problem how to identify multiple global local optima and how to maintain the identified optima till the end of the search for most of the multi modal optimization algorithms the fine local search capabilities are not effective if the required accuracy is high these algorithms fail to find the desired optima even after converging near them to overcome this problem this paper integrates a novel local search technique with some existing pso based multimodal optimization algorithms to enhance their local search ability the algorithms are tested on 14 commonly used multi modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations

poset embeddings of hilbert functions and betti numbers abstract we study inequalities between graded betti numbers of ideals in a standard graded algebra over a field and their images under embedding maps defined earlier by us in caviglia and kummini 2013 5 we show that if graded betti numbers do not decrease when we replace ideals in an algebra by their embedded versions then the same behaviour is carried over to ring extensions as a corollary we give alternative inductive proofs of earlier results of bigatti hulett pardue mermin peeva stillman and murai we extend a hypersurface restriction theorem of herzog and popescu to the situation of embeddings we show that we can obtain the betti table of an ideal in the extension ring from the betti table of its embedded version by a sequence of consecutive cancellations we further show that the lex plus powers conjecture of evans reduces to the artinian situation