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Entropy-based multi-objective genetic algorithm for design optimization
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AI
DOI：10.1007/s00158-002-0247-6.png)
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Abstract
En 中文
Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, one that aims at obtaining the Pareto solutions with maximum possible coverage and uniformity along the Pareto frontier. The new method, called an Entropy-based MOGA (or E-MOGA), is based on an application of concepts from the statistical theory of gases to a baseline MOCA. Two demonstration examples, the design of a two-bar truss and a speed reducer, are used to demonstrate the effectiveness of E-MOGA in comparison to the baseline MOGA.
Keywords:
multiobjective optimization
genetic algorithms
entropy
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