Results 271 to 280 of about 299,863 (342)

A coevolutionary multi-objective evolutionary algorithm

The 2003 Congress on Evolutionary Computation, 2003. CEC '03., 2004
In this paper, we propose a first version of a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts. The primary design goal of the proposed approach is to reduce the total number of objective function evaluations required to produce a reasonable good approximation of the true Pareto front of a problem. The main idea of
C.A. Coello Coello, M.R. Sierra
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Survey of multi objective evolutionary algorithms

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], 2015
Multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. Multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA.
Vimal L. Vachhani, Vipul K. Dabhi, Harshadkumar B. Prajapati   +2 more
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A parallel Multi-objective Evolutionary Algorithm

2010 The 2nd International Conference on Industrial Mechatronics and Automation, 2010
Multi-objective Evolutionary Algorithm can be applied to problems in Economies, Management and Engineering; actually, most of design problem in real world can be reduced to multi-objective optimization problem. This paper proposed a new Parallel Evolutionary Algorithm based multi-objective optimal algorithm.
null Zhang Wenpeng, null Wang Xing
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Multi-Objective Evolutionary Algorithms

2015
Evolutionary algorithms (EA s) have amply shown their promise in solving various search and optimization problems for the past three decades. One of the hallmarks and niches of EAs is their ability to handle multi-objective optimization problems in their totality, which their classical counterparts lack.
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Introduction to Multi-objective Evolutionary Algorithms

2014
Many real engineering optimization problems can be modeled as multi-objective optimization problem (MOP). These problems actually do have multiple objectives that conflict each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives.
M. C. Bhuvaneswari, G. Subashini
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Interval Robust Multi-Objective Evolutionary Algorithm

2009 IEEE Congress on Evolutionary Computation, 2009
Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the ...
G. L. Soares, F. G. Guimaraes, C. A. Maia, J. A. Vasconcelos, L. Jaulin   +4 more
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Robustness using Multi-Objective Evolutionary Algorithms

2006
In this work a method to take into account the robustness of the solutions during multi-objective optimization using a Multi-Objective Evolutionary Algorithm (MOEA) was presented. The proposed methodology was applied to several benchmark single and multi-objective optimization problems.
A. Gaspar-Cunha, J. A. Covas
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Dynamical multi-objective optimization evolutionary algorithm

SPIE Proceedings, 2003
A dynamical multi-objective evolutionary algorithm (DMOEA) is proposed. It is the first study of the dynamical evolutionary algorithm (DEA) in multi-objective optimization process. All individuals called as particles in a population evolve through a new selection mechanism. We combine the selection mechanism in DEA and the elitists strategy in existing
Shengwu Xiong, Feng Li, Weiwu Wang, Feng Chen   +3 more
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Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

Computers & industrial engineering, 2022
Rui Li, Wenyin Gong, Chao Lu
semanticscholar   +1 more source

Evolutionary Multi-objective Whale Optimization Algorithm

2019
Whale Optimization Algorithm (WOA) is a recently proposed metaheuristic algorithm and achieved much attention of the researchers worldwide for its competitive performance over other popular metaheuristic algorithms. As a metaheuristic algorithm, it mimics the hunting behavior of humpback whale which uses its unique spiral bubble-net feeding maneuver to
Faisal Ahmed Siddiqi, Chowdhury Mofizur Rahman   +1 more
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