Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Bhuvana, J; Aravindan, Chandrabose

doi:10.1007/s00500-015-1593-9

Cited by 19 publications

(4 citation statements)

References 53 publications

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“…Let {x k } be a linked sequence, then it admits limit points and every limit point is Pareto-stationary for Problem (2).…”

Section: Multi-objective Descent Methodsmentioning

confidence: 99%

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A Memetic Procedure for Global Multi-Objective Optimization

Lapucci¹,

Mansueto²,

Schoen³

2022

Preprint

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In this paper we consider multi-objective optimization problems over a box. The problem is very relevant and several computational approaches have been proposed in the literature. They broadly fall into two main classes: evolutionary methods, which are usually very good at exploring the feasible region and retrieving good solutions even in the nonconvex case, and descent methods, which excel in efficiently approximating good quality solutions. In this paper, first we confirm, through numerical experiments, the advantages and disadvantages of these approaches. Then we propose a new method which combines the good features of both. The resulting algorithm, which we call Non-dominated Sorting Memetic Algorithm (NSMA), besides enjoying interesting theoretical properties, excels in all of the numerical tests we performed on several, widely employed, test functions.

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“…Let {x k } be a linked sequence, then it admits limit points and every limit point is Pareto-stationary for Problem (2).…”

Section: Multi-objective Descent Methodsmentioning

confidence: 99%

“…Even the few proposed strategies employing gradient information for the local search steps do not exploit the concept of common descent directions. Rather, convex combinations of gradients are generated and exploited in various ways [2,3,34,37,47].…”

Section: Introductionmentioning

confidence: 99%

A Memetic Procedure for Global Multi-Objective Optimization

Lapucci¹,

Mansueto²,

Schoen³

2022

Preprint

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“…For solving this optimization model we will use the memetic algorithm (MA), a useful tool for dealing with large-scale combinational problem [24][25][26][27] . Coming from the concept of meme, MA is defined as a part of local improvement in the process of cultural evolution.…”

Section: Memetic Algorithmmentioning

confidence: 99%

Identifying vital edges in Chinese air route network via memetic algorithm

Wei

Liang

Yan

et al. 2017

Chinese Journal of Aeronautics

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Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systemsPeer ReviewedPostprint (published version

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“…The existing local search algorithm is improved from the hill climbing method. Local search algorithm has been widely used, and many improved algorithms [39][40][41][42] have been derived. However, the simple local search algorithm has an important defect that can not be solved.…”

Section: Introductionmentioning

confidence: 99%

An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making

Zhang

2019

Applied Sciences

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Enterprise investment decision-making should not only consider investment profits, but also investment risks, which is a complex nonlinear multi-objective optimization problem. However, traditional investment decisions often only consider profit as a goal, resulting in an incorrect decision. Facing the high complexity of investment decision-making space, traditional multi-objective optimization methods pay too much attention to global search ability because of pursuing convergence speed and avoiding falling into local optimum, while local search ability is insufficient, which makes it difficult to converge to the Pareto optimal boundary. To solve this problem, an improved SPEA2 algorithm is proposed to optimize the multi-objective decision-making of investment. In the improved method, an external archive set is set up separately for local search after genetic operation, which guarantees the global search ability and also has strong local search ability. At the same time, the new crossover operator and individual update strategy are used to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population. The experimental results show that the improved method can converge to the Pareto optimal boundary and improve the convergence speed, which can effectively realize the multi-objective decision-making of investment.

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Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Cited by 19 publications

References 53 publications

A Memetic Procedure for Global Multi-Objective Optimization

A Memetic Procedure for Global Multi-Objective Optimization

Identifying vital edges in Chinese air route network via memetic algorithm

An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making

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