A multi-objective genetic local search algorithm and its application to flowshop scheduling

Ishibuchi, Hisao; Murata, Tadahiko

doi:10.1109/5326.704576

Cited by 853 publications

(412 citation statements)

References 16 publications

Supporting

Mentioning

405

Contrasting

Unclassified

Order By: Relevance

“…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%

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

Ishibuchi

Shibata

2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

Ishibuchi

Shibata

2003

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Chang et al 33 studies the gradual-priority weighting approach in place of the variable weight approach for genetic and genetic local search methods. These two methods are related to those of Murata et al 34 and Ishibuchi and Murata 35 , respectively. In numerical experiments, the gradual-priority weighting approach is shown superior.…”

Section: Weighted Objectives Approachesmentioning

confidence: 94%

“…The authors also test their proposed GA with three objectives including flowtime. Later, in Ishibuchi and Murata 35 the algorithm is extended by using a local search step that is applied to every new solution, after the crossover and mutation procedures.…”

Section: Pareto Approachesmentioning

confidence: 99%

“…The new CMOGA outperforms MOGA in the experiments carried out. Ishibuchi et al 49 present a comprehensive study about the effect of adding local search to their previous algorithm 35 . The local search is only applied to good individuals and by specifying search directions.…”

Section: Pareto Approachesmentioning

confidence: 99%

“…These algorithms start from heuristic solutions that are further enhanced by improvement schemes. Two versions of these SA (MOSA and MOSA-II) are shown to outperform the GA of Ishibuchi and Murata 35 . Pasupathy et al 58 have proposed a Paretoarchived genetic algorithm with local search and have tested it with the makespan and flowtime objectives.…”

Section: Pareto Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

View full text Add to dashboard Cite

Memetic Algorithms: A Contemporary Introduction

Cotta

Gallardo

Mathieson

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

Memetic algorithms (MAs) constitute a search and optimization paradigm based on the orchestrated interplay between global and local search components, and have the exploitation of specific problem knowledge as one of their central tenets. MAs can take different forms although a classical incarnation involves the integration of independent search processes within a population‐based optimization approach. We discuss this basic structure as well as several of the issues arising in the design process. This paves the way for providing a glimpse of some algorithmic extensions of this basic scheme. After providing an overview of the numerous practical applications of MAs, we close this article with some current perspectives of these techniques.

show abstract

A multi-objective genetic local search algorithm and its application to flowshop scheduling

Cited by 853 publications

References 16 publications

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization

Review on Job-Shop and Flow-Shop Scheduling Using

Memetic Algorithms: A Contemporary Introduction

Contact Info

Product

Resources

About