Abstract
In recent years, methods of multi-objective evolutionary algorithms (MOEAs) have been developed to solve problems involving the satisfaction of multiple objectives within the limits of certain constraints, yet there still exists some uncertainty about finding a generally trustworthy method that can consistently find solutions which are really close to desired objectives in all situations. In this study, a combined Pareto multi-objective differential evolution (CPMDE) algorithm is presented. The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel selection scheme at each generation. The ability of CPMDE in solving unconstrained, constrained and real-world optimization problems was demonstrated. Competitive results obtained from benchmarking CPMDE suggest that it is a good alternative for solving real multi-objective optimization problems.
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Olofintoye, O., Adeyemo, J., Otieno, F. (2014). A Combined Pareto Differential Evolution Approach for Multi-objective Optimization. In: Schuetze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Studies in Computational Intelligence, vol 500. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01460-9_10
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DOI: https://doi.org/10.1007/978-3-319-01460-9_10
Publisher Name: Springer, Heidelberg
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