Multi-Objective Memetic Algorithms by Gideon Avigad (auth.), Chi-Keong Goh, Yew-Soon Ong, Kay Chen

By Gideon Avigad (auth.), Chi-Keong Goh, Yew-Soon Ong, Kay Chen Tan (eds.)

The software of refined evolutionary computing methods for fixing complicated issues of a number of conflicting ambitions in technology and engineering have elevated gradually within the contemporary years. inside of this growing to be development, Memetic algorithms are, possibly, some of the most winning tales, having proven greater efficacy in facing multi-objective difficulties in comparison to its traditional opposite numbers. still, researchers are just starting to notice the giant power of multi-objective Memetic set of rules and there stay many open subject matters in its design.

This e-book offers a really first finished number of works, written by means of top researchers within the box, and displays the present state of the art within the conception and perform of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is equipped for a large readership and may be a worthy reference for engineers, researchers, senior undergraduates and graduate scholars who're drawn to the parts of Memetic algorithms and multi-objective optimization.

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It is well-recognized that evolutionary algorithms are suitable for multiobjective optimization because a number of non-dominated solutions can be simultaneously obtained by their single run. Currently evolutionary multiobjective optimization (EMO) is one of the most active research areas in the field of evolutionary computation. Whereas a large number of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature [2], we do not have many studies on memetic algorithms for multiobjective optimization.

The performance of MOGLS was examined for flowshop scheduling [14]. A variant of MOGLS with higher search ability was proposed by Jaszkiewicz [18]. , SPEA [32]). M-PAES (memetic Pareto archived evolution strategy) by Knowles and Corne [21] is an MOMA where Pareto dominance is used for comparing the current solution and its neighbor in local search. When they are non-dominated with each other, they are compared using a crowding measure based on a grid-type partition of the objective space. The performance of M-PAES was examined for multiobjective knapsack problems in [21] and degree-constrained multiobjective minimum-weight spanning tree problems in [22].

S. C. , SCI 171, pp. 27–49. com 28 H. Ishibuchi et al. Whereas most memetic algorithms have been developed for single-objective optimization, real-world application problems usually involve multiple objectives. It is well-recognized that evolutionary algorithms are suitable for multiobjective optimization because a number of non-dominated solutions can be simultaneously obtained by their single run. Currently evolutionary multiobjective optimization (EMO) is one of the most active research areas in the field of evolutionary computation.

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