Genetic local search for multi-objective combinatorial optimization

Jaszkiewicz, Andrzej

doi:10.1016/s0377-2217(01)00104-7

Cited by 442 publications

(253 citation statements)

References 17 publications

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“…The distance between two parents is measured in the decision space or the objective space. The necessity of mating restriction in EMO algorithms was also stressed by Jaszkiewicz [13] and Watanabe et al [18]. On the other hand, Zitzler & Thiele [20] reported that no improvement was achieved by mating restriction in their computational experiments.…”

Section: Introductionmentioning

confidence: 96%

“…Recent EMO algorithms usually share some common ideas such as elitism, fitness sharing and Pareto ranking for improving both the diversity of solutions and the convergence speed to the Pareto-front (e.g., see Coello et al [1] and Deb [3]). In some studies, local search was combined with EMO algorithms for further improving the convergence speed to the Pareto-front [10,[12][13][14]. While mating restriction has been often discussed in the literature, its effect has not been clearly demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

Ishibuchi

Shibata

2003

Lecture Notes in Computer Science

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Abstract. This paper proposes a new mating scheme for evolutionary multiobjective optimization (EMO), which simultaneously improves the convergence speed to the Pareto-front and the diversity of solutions. The proposed mating scheme is a two-stage selection mechanism. In the first stage, standard fitness-based selection is iterated for selecting a pre-specified number of candidate solutions from the current population. In the second stage, similarity-based tournament selection is used for choosing a pair of parents among the candidate solutions selected in the first stage. For maintaining the diversity of solutions, selection probabilities of parents are biased toward extreme solutions that are different from prototypical (i.e., average) solutions. At the same time, our mating scheme uses a mechanism where similar parents are more likely to be chosen for improving the convergence speed to the Paretofront. Through computational experiments on multi-objective knapsack problems, it is shown that the performance of recently proposed well-known EMO algorithms (SPEA and NSGA-II) can be improved by our mating scheme.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

Ishibuchi

Shibata

2003

Lecture Notes in Computer Science

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“…The traditional GA usually consists of some operators such as crossover, selection and mutation which are simulated from biological and genetic processes. However, the traditional GA is inefficient for solving large optimization problems [2].…”

Section: Introductionmentioning

confidence: 99%

Tsp Solving by Hybridization of GA and AS

Joudaki¹,

Imani²,

Mazhari³

2012

IJCEE

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Abstract-Traveling salesman problem (TSP) is one of the most famous combinational optimization problems. Today, many solutions have been offered by using different methods to solve this problem. Each one of these solutions has its own advantages and disadvantages and a comprehensive solution which proves itself as the most optimum one is not presented yet. But we are still waiting for better solutions which solve the problem in more optimum ways. In this paper we have proposed a hybridization solution using the two GA and AS for TSP solving and we have called it GA-AS. The results of this new solution which is presented in experimental results, shows that TSP is solved by using our proposed combinational solution (GA-AS) has better results than TSP solved only by using standard GA. Another new idea considered in this paper is to change the current GA generation in order to reach a better generation and therefore a better answer which is explained schematically.

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“…An early example is VEGA [58]; other examples include the algorithms proposed by Ishibuchi and Murata [38] and MOGLS of Jaszkiewicz [39]. Also ACO algorithms frequently use some form of scalarized aggregation, for example, for combining pheromone (or heuristic) information specific to each objective [5,28,47].…”

Section: Scalarization-based Multi-objective Optimizationmentioning

confidence: 99%

“…The single-objective algorithm used to tackle the scalarized problems is an iterated local search algorithm, and the neighborhood operator is based on 2-Opt moves. This hybrid algorithm has been compared favorably to the best algorithm known at that time, the MOGLS algorithm from Jaszkiewicz [39]. A more in-depth experimental study of PD-TPLS and other TPLS variants has been presented by Paquete and Stützle [55].…”

Section: Hybrid Tpls+plsmentioning

confidence: 99%

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

Dubois-Lacoste

López-Ibáñez

Stützle

2013

Hybrid Metaheuristics

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In this chapter, we review metaheuristics for solving multi-objective combinatorial optimization problems, when no information about the decision maker's preferences is available, that is, when problems are tackled in the sense of Pareto optimization. Most of these metaheuristics follow one of the two main paradigms to tackle such problems using metaheuristics. The first paradigm is to rely on Pareto dominance when exploring the search space. The second paradigm is to tackle several single-objective problems to find several solutions that are non-dominated for the original problem; in this case, one may exploit existing, efficient single-objective algorithms, but the performance depends on the definition of the set of scalarized problems. There are also a number of approaches in the literature that combine both paradigms. However, this is usually done in a relatively ad-hoc way. In this paper, we review two conceptually simple methods representative of each paradigm: Pareto local search and Two-phase Local Search. The hybridization of these two strategies provides a general framework for engineering stochastic local search algorithms that can be used to improve over the state-of-the-art for several, widely studied problems.

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Genetic local search for multi-objective combinatorial optimization

Cited by 442 publications

References 17 publications

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

Tsp Solving by Hybridization of GA and AS

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

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