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An evolutionary algorithm for multiple criteria problems

Soylu, Banu
In this thesis, we develop an evolutionary algorithm for approximating the Pareto frontier of multi-objective continuous and combinatorial optimization problems. The algorithm tries to evolve the population of solutions towards the Pareto frontier and distribute it over the frontier in order to maintain a well-spread representation. The fitness score of each solution is computed with a Tchebycheff distance function and non-dominating sorting approach. Each solution chooses its own favorable weights according to the Tchebycheff distance function. Some seed solutions at initial population and a crowding measure also help to achieve satisfactory results. In order to test the performance of our evolutionary algorithm, we use some continuous and combinatorial problems. The continuous test problems taken from the literature have special difficulties that an evolutionary algorithm has to deal with. Experimental results of our algorithm on these problems are provided. One of the combinatorial problems we address is the multi-objective knapsack problem. We carry out experiments on test data for this problem given in the literature. We work on two bi-criteria p-hub location problems and propose an evolutionary algorithm to approximate the Pareto frontiers of these problems. We test the performance of our algorithm on Turkish Postal System (PTT) data set (TPDS), AP (Australian Post) and CAB (US Civil Aeronautics Board) data sets. The main contribution of this thesis is in the field of developing a multi-objective evolutionary algorithm and applying it to a number of multi-objective continuous and combinatorial optimization problems.