Performance of hybrid genetic algorithm for the grey pattern problem
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A hybrid genetic algorithm that uses a new kind of solution recombination operators − a so-called multiple parent crossover on the grey pattern problem, which is as special case of the well-known problem, the quadratic assignment problem.Abstract:
Recently, genetic algorithms (GAs) are quite popular by solving combinatorial optimization problems. In this paper, we discuss a hybrid genetic algorithm that uses a new kind of solution recombination operators − a so-called multiple parent crossover. We examined this innovative crossover operator on the grey pattern problem, which is as special case of the well-known problem, the quadratic assignment problem. The results obtained during the experimentation with the set of 62 instances of the grey pattern problem demonstrate promising efficiency of the multiple parent crossover. All the instances tested were solved to pseudo-optimality within surprisingly small computation times.read more
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Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach
Faizal Hafiz,Adel Abdennour +1 more
TL;DR: A new probability-based approach is proposed for the learning in discrete Particle Swarm Optimization and a generic framework is developed to discretize PSO and its variants, to make them suitable for combinatorial optimization problems.
Journal ArticleDOI
Comparison of crossover operators for the quadratic assignment problem
TL;DR: The basic conceptual features and specific characteristics of various crossover operators in the context of the quadratic assignment problem (QAP) are discussed and MPX operator, so-called multiple parent crossover (MPX), enables to achieve better solutions than other operators tested.
Journal ArticleDOI
Incorporating a modified uniform crossover and 2-exchange neighborhood mechanism in a discrete bat algorithm to solve the quadratic assignment problem
TL;DR: This paper introduces a novel discrete variant of bat algorithm to deal with the quadratic assignment problem, a well-known NP-hard problem in combinatorial optimization.
Journal ArticleDOI
A hybrid co-evolutionary genetic algorithm for multiple nanoparticle assembly task path planning
TL;DR: In this paper, a hybrid co-evolutionary genetic algorithm (HCGA) has been presented for determining the optimal moving paths of several nanoparticles in a complex environment, where an artificial potential field has been used to determine the feasible initial paths for moving the nanoparticles.
A Simple Genetic Algorithm using Sequential Constructive Crossover for the Quadratic Assignment Problem
TL;DR: In this paper, the authors modify the sequential constructive crossover (SCX) operator for a simple GA to find heuristic solution to the quadratic assignment problem (QAP).
References
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TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.