A Distance Function-Based Multi-Objective Evolutionary Algorithm
نویسندگان
چکیده
A multi-objective evolutionary algorithm (MOEA) approach is presented in this paper. The algorithm (DFBMOEA) aims to improve convergence of Paretobased MOEAs to the true Pareto optimal set/Pareto front and remove decision maker interaction from the process. A novel distance function is used as a fitness function for MOEA. A range equalisation function and a reference vector are utilised to eliminate the prior knowledge required from decision makers. A 0 & 1 knapsack problem [27] was tested to demonstrate the performance of our approach compared to two leading MOEAs: Non-dominated Sorting Genetic Algorithm II (NSGA II) and Strength Pareto EA II (SPEA II). The results show that our approach produced a set of effective Pareto optimal solutions that are comparable to the two leading MOEA s.
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