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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Proceedings ArticleDOI
Some discussions about MOGAs: individual relations, non-dominated set, and application on automatic negotiation
TL;DR: This paper studies the relations of individuals in evolutionary populations, and then investigates some features of the relations, and proposes a multi-objective genetic algorithm (MOGA) based on quick sort, which is called QKMogA, and applies it on automatic negotiation for agents.
Journal Article
An Approach of Constructing Multi-Objective Pareto Optimal Solutions Using Arena’s Principle
TL;DR: It is proved that the arena’s principle works correctly and its computational complexity is O(rmN) (0m/N1), and experimental results indicate that AP performs better than the other two algorithms on the CPU time efficiency.
Proceedings ArticleDOI
An Efficient Multi-Objective Evolutionary Algorithm for Combinational Circuit Design
TL;DR: An efficient multi-objective evolutionary algorithm (EMOEA) to design circuits based on non-dominated set for keeping diversity of the population and therefore, avoids trapping in local optimal.
Journal ArticleDOI
A novel multi-objective genetic algorithm based error correcting output codes
TL;DR: Results show that compared with other algorithms, the proposed multi-objective genetic algorithm (GA) based error correcting output codes (ECOC) with setting accuracy and diversity as two objectives obtains higher performance in most cases due to the trade-off between performance and diversity.
Journal ArticleDOI
Linear programming-based directed local search for expensive multi-objective optimization problems: Application to drinking water production plants
TL;DR: A new neighborhood-based iterative LS method, relying on first derivatives approximation and linear programming (LP), aiming to steer the search along any desired direction in the objectives space is proposed, which clearly outperforms the directed search approach.