The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance

Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of the NS, including a statistical analysis, demonstrated a nonproportional increase in the performance of DABC with some NS. For SA the improvement of the solutions appeared to be more uniform with an almost nonexistent variance in improvement.

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title: SIMULATED ANNEALING METHODS WITH GENERAL ACCEPTANCE PROBABILITIES
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title: Simulated annealing: A tool for operational research
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title: Temperature measurement and equilibrium dynamics of simulated annealing placements
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title: Parameter Tuning for the Artificial Bee Colony Algorithm
abstract: While solving a problem by an optimization algorithm, adjusting algorithm parameters have significant importance on the performance of the algorithm. A fine tuning of control parameters is required for most of the algorithms to obtain desired solutions. In this study, performance of the Artificial Bee Colony (ABC) algorithm, which simulates the foraging behaviour of a honey bee swarm, was investigated by analyzing the effect of control parameters.

title: Parameter tuning of artificial bee colony algorithm for Gaussian noise elimination on digital images
abstract: In this paper, the control parameters of the artificial bee colony algorithm were examined to determine for the best performance of the noise elimination problem on gray level digital images. In order to eliminate a noise, a two dimensional finite impulse response digital filter was designed and the artificial bee colony algorithm was used to adjust its coefficient matrix. For the best selected control parameters, the designed two dimensional finite impulse response digital filter was used to eliminate the Gaussian noise on the gray level digital images at different noise densities and the performance of the designed filter was compared in terms of the noise tolerance.

title: The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems
abstract: Artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention in 2005, many ABC models have been proposed in order to solve different optimization problems. In all the models proposed, there are only one scout bee and a constant limit value used as control parameters for the bee population. In this study, the performance of ABC algorithm on the numeric optimization problems was analyzed by using different number of scout bees and limit values. Experimental results show that the results obtained by using more than one scout bee and different limit values, are better than the results of basic ABC. Therefore, the control parameters of the basic ABC should be tuned according to given class of optimization problems. In this paper, we propose reasonable value ranges of control parameters for the basic ABC in order to obtain better results on the numeric optimization problems.

title: A hybrid discrete artificial bee colony algorithm for permutation flowshop scheduling problem
abstract: To minimize the makespan in permutation flowshop scheduling problems, a hybrid discrete artificial bee colony (HDABC) algorithm is presented. In the HDABC, each solution to the problem is called a food source and represented by a discrete job permutation. First, the initial population with certain quality and diversity is generated from Greedy Randomized Adaptive Search Procedure (GRASP) based on Nawaz-Enscore-Ham (NEH) heuristics. Second, the discrete operators and algorithm, such as insert, swap, path relinking and GRASP are applied to generate new solution for the employed bees, onlookers and scouts. Moreover, local search is applied to the best one. The presented algorithm is tested on scheduling problem benchmarks. Experimental results show its efficiency.

title: A survey of very large-scale neighborhood search techniques
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title: TSPLIB - A Traveling Salesman Problem Library
abstract: This paper contains the description of a traveling salesman problem library (TSPLIB) which is meant to provide researchers with a broad set of test problems from various sources and with various properties. For every problem a short description is given along with known lower and upper bounds. Several references to computational tests on some of the problems are given.

title: Comparison of Metaheuristics
abstract: Metaheuristics are truly diverse in nature—under the overarching theme of performing operations to escape local optima, algorithms as different as ant colony optimization, tabu search, harmony search, and genetic algorithms have emerged. Due to the unique functionality of each type of metaheuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this chapter, we discuss techniques for meaningful comparison of metaheuristics. We discuss how to create and classify instances in a new testbed and how to make sure other researchers have access to the problems for future metaheuristic comparisons. Further, we discuss the disadvantages of large parameter sets and how to measure complicating parameter interactions in a metaheuristic’s parameter space. Last, we discuss how to compare metaheuristics in terms of both solution quality and runtime.