Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
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Computers and Structures
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Test-case generator for nonlinear continuous parameter optimizationtechniques
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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The uncertainty in many engineering problems can be handled through probabilistic, fuzzy, or interval methods. This paper aims to use a hybrid genetic algorithm for tackling such problems. The proposed hybrid algorithm integrates a simple local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective evolutionary algorithm. The work demonstrates the use of a technique alternating between optimization (general GA) and anti-optimization (local search). Local search utilizes specialized search engines that allow users to submit constrained searches. The algorithm has been tuned and its performance evaluated through specially formulated test problems referred to as 'Target Matching Problems' with multiple objectives. The results obtained indicate that the approach can produce good results at reasonable computational costs.