Improving the Performance of Metaheuristics: An Approach Combining Response Surface Methodology and Racing Algorithms

Abstract

The setup of heuristics and metaheuristics, that is, the fine-tuning of their parameters, exercises a great influence in both the solution process, and in the quality of results of optimization problems. The search for the best fit of these algorithms is an important task and a major research challenge in the field of metaheuristics. The fine-tuning process requires a robust statistical approach, in order to aid in the process understanding and also in the effective settings, as well as an efficient algorithm which can summarize the search process. This paper aims to present an approach combining design of experiments (DOE) techniques and racing algorithms to improve the performance of different algorithms to solve classical optimization problems. The results comparison considering the default metaheuristics and ones using the settings suggested by the fine-tuning procedure will be presented. Broadly, the statistical results suggest that the fine-tuning process improves the quality of solutions for different instances of the studied problems. Therefore, by means of this study it can be concluded that the use of DOE techniques combined with racing algorithms may be a promising and powerful tool to assist in the investigation, and in the fine-tuning of different algorithms. However, additional studies must be conducted to verify the effectiveness of the proposed methodology.

Reduced Complexity Optimal Resource Allocation for Enhanced Video Quality in a Heterogeneous Network Environment

“…Belonging to the latter category, SA was chosen to solve MKP. SA is a probabilistic technique used to find a global minimum of an objective function by progressing through many local minima [11]. Other approximation meta-heuristics algorithms, such as GA and PSO, can be used to solve the MKP.…”

“…Its ease of implementation and its convergence properties made it an algorithm of choice for solving combinatorial optimization problems like MKP. It was named as such because of its similarity to the physical solid annealing process [11], which involves heating and controlled cooling of material by varying the temperature. If the temperature decreases very slowly, a stable state can be observed, which cannot be reached if the temperature falls quickly [11].…”

“…where α, β, ...,ζ are parameters of algorithm A for a given problem P and 1 ... η α , 1 ... η β , 1 ... η ζ are the finite ranges of values assumed for each parameter. The number of parameters as well as their ranges can vary extensively according to A and P studied, such that η α x η β x ... x η ζ should be possibly a number of combinations tests of A on P [11].…”

Modified self-adaptive local search algorithm for a biobjective permutation flow shop scheduling problem

“…Automatic algorithm configuration has increased interest in offline techniques, and it incorporates experimental design and statistical modeling techniques [5][6][7][8][9], racing algorithms [10][11][12][13], and metaoptimization approaches, which tune the parameters using any other heuristic [14][15][16][17][18][19][20].…”

Performance analysis

“…Within the last decade, researchers have been constantly evolving, working to cover theoretical developments and enhance techniques. These developments assist researchers in obtaining solutions for optimization problems and reduce the associated work (Barbosa et al, 2015). One of two main directions can be taken: efficiently testing with fixed control parameters or parameter tuning.…”