Multi-Objective Optimization Using Differential Evolution
نویسندگان
چکیده
In most real world multi-objective optimization problems the objectives are conflicting and therefore, they do not lend themselves to a single solution but result in a set of non-dominating solutions. Several issues arise in Multi-objective Optimization. Firstly, the entire search space has to be searched (in order to find all the good nondominated solutions) without getting stuck in local optima, secondly, the search should approach the global Paretooptimal front as closely as possible, thirdly, the search should also ensure a good spread of solutions along the obtained pareto-optimal front and fourthly, it should achieve convergence in a reasonable time. Therefore, the search algorithm has to be carefully designed to address the above mentioned issues. In this work, Elitist Multi-objective Differential Evolution (E-MODE) a new Multi-objective Optimization algorithm is designed and implemented for the solution of real parameter multi-objective function optimization. Keywords—Differential Evolution, Optimization, multi-Objective
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