Conference Paper

A Multi-objective Tabu Search Algorithm for Constrained Optimisation Problems.

DOI: 10.1007/978-3-540-31880-4_34 Conference: Evolutionary Multi-Criterion Optimization, Third International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, Proceedings
Source: DBLP

ABSTRACT Real-world engineering optimisation problems are typically multi-objective and highly constrained, and constraints may be
both costly to evaluate and binary in nature. In addition, objective functions may be computationally expensive and, in the
commercial design cycle, there is a premium placed on rapid initial progress in the optimisation run. In these circumstances,
evolutionary algorithms may not be the best choice; we have developed a multi-objective Tabu Search algorithm, designed to
perform well under these conditions. Here we present the algorithm along with the constraint handling approach, and test it
on a number of benchmark constrained test problems. In addition, we perform a parametric study on a variety of unconstrained
test problems in order to determine the optimal parameter settings. Our algorithm performs well compared to a leading multi-objective
Genetic Algorithm, and we find that its performance is robust to parameter settings.

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    ABSTRACT: This first chapter intends to review and analyze the powerful new Harmony Search (HS) algorithm in the context of metaheuristic algorithms. I will first outline the fundamental steps of Harmony Search, and how it works. I then try to identify the characteristics of metaheuristics and analyze why HS is a good meta-heuristic algorithm. I then review briefly other popular metaheuristics such as par-ticle swarm optimization so as to find their similarities and differences from HS. Finally, I will discuss the ways to improve and develop new variants of HS, and make suggestions for further research including open questions. Comment: 14 pages in typeset publications
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    ABSTRACT: Most real world search and optimization problems naturally involve multiple responses. In this paper we investigate a multiple response problem within desirability function framework and try to determine values of input variables that achieve a target value for each response through three meta-heuristic algorithms such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS). Each algorithm has some parameters that need to be accurately calibrated to ensure the best performance. For this purpose, a robust calibration is applied to the parameters by means of Taguchi method. The computational results of these three algorithms are compared against each others. The superior performance of SA over TS and TS over GA is inferred from the obtained results in various situations.
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