Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Mechanical Component Design for Multiple Objectives Using Elitist Non-dominated Sorting GA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A Particle Swarm Algorithm for Multiobjective Design Optimization
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On simultaneous perturbation particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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This paper presents a new adaptive strategy for combining global (exploration) and local (exploitation) search capabilities of a multi-objective particle swarm optimizer (MOPSO).The goal of hybridization of search strategies is to enhance an optimizer's overall performance. In contrast to previous attempts at hybridization, the proposed methodology efficiently balances exploration and exploitation of the search space using the two novel methods of intersection test and objective function normalization. Experimental results obtained from several well-known test cases demonstrate the efficiency of the proposed MOPSO algorithm. The results are compared with those obtained from NSGA-II, which is a well-established evolutionary algorithm.