Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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A non dominated ranking Multi Objective Genetic Algorithm and electre method for unequal area facility layout problems
TL;DR: This paper addresses the unequal area facility layout problem of UA-FLP by employing Multi Objective Genetic Algorithm (MOGA) implementing four separate fitness functions within a Pareto evolutionary procedure, and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre.
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Review Article: Multi-objective nature-inspired clustering and classification techniques for image segmentation
Chin-Wei Bong,Mandava Rajeswari +1 more
TL;DR: This paper aims to provide a comprehensive review of nature-inspired techniques used in image segmentation problems, particularly on multi-objective clustering and classification approaches, and describes issues related to diversity measures, accuracy measures, rule manipulation, and managing uncertainties.
Journal ArticleDOI
A New Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization
Ke Shang,Hisao Ishibuchi +1 more
TL;DR: A new hypervolume-based evolutionary multiobjective optimization algorithm (EMOA), namely, R2HCA-EMOA (R2-based hypervolume contribution approximation EMOA), is proposed for many-objectives optimization and is superior to all the compared state-of-the-art EMOAs.
Journal ArticleDOI
Multi-objective optimisation of the cure of thick components
TL;DR: In this article, a multi-objective optimisation of the cure stage of composites manufacture is addressed, aiming to minimize the cure process duration and maximum temperature overshoot within the curing part by selecting an appropriate thermal profile.
Proceedings ArticleDOI
A Dynamic Multi-Objective Evolutionary Algorithm Based on an Orthogonal Design
TL;DR: This article introduces a dynamic orthogonal multi-objective evolutionary algorithm called "DOMOEA", that generalizes an earlier paper of the authors' (OMOEA-II) to dynamic environments and applies an "orthogonal design method" to enhance the fitness of the population during the static stages between two successive changes of environment.