Li Cao, Maocai Wang, Massimiliano Vasile, Guangming Dai, Huanqin Wu
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引用次数: 0
Abstract
This article proposes two novel mechanisms to improve the performance of many-objective evolutionary algorithms based on Chebyshev scalarization. One mechanism improves the efficiency and effective...
期刊介绍：
Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process.
Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.
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