The Improvement Of Multi-objective Evolutionary Algorithm Based On Decomposition And Multi-objective Wolf Pack Algorithm

Multi-objective optimization problems widely occur in the fields science,en-gineering,economics and so on.With the development of science and technology,the practical problems are more complicated,such that the multi-objective opti-mization problem is hard to solve.Thus,it has very important theoretical and application value to study the effective approaches for multi-objective optimiza-tion problems.Unlike traditional optimization methods that often have many limited conditions in the objective function such as continuity or differentiabil-ity,the multi-objective evolutionary algorithm can obtain uniformly distributed set of non-dominated solutions.In past years,many multi-objective evolution-ary algorithms have been proposed to solve the complex optimization problems,such as fast and elitist multi-objective genetic algorithm,improving the strength Pareto evolutionary algorithm for multi-objective optimization,multi-objective evolutionary algorithm based on decomposition and multi-objective grey wolf optimizer and so on.In particular,the multi-objective evolutionary algorithm based on decom-position employed decomposition strategy in multi-objective evolutionary opti-mization,which provides a new direction for multi-objective optimization in var-ious fields,and becomes an effective tool to solve complex optimization problem-s.Multi-objective grey wolf optimizer is a novel swarm intelligence optimization algorithm,which inspire by the social leadership and hunting technique of grey wolves.Even if it has less parameters and easy operation.Moreover,it also could balance global exploration and local exploitation,the multi-objective evolution-ary algorithm based on decomposition and multi-objective grey wolf optimizer still exists some weakness.To enhance their performance,the main content of this thesis describes as follows:1.An achievement scalarizing function search-based multi-objective evolu-tionary algorithm is proposed to overcome the decrease of population diversi-ty and premature convergence of multi-objective evolutionary algorithm based on decomposition caused by the fixed population size and set of weight vectors during the evolutionary process.In the proposed algorithm,the smaller initial population is first set,and a search strategy based on achievement scalarizing function with adaptive preference and search is designed to enhance the search of the sparse region and dynamically increase the population size and weight vector.Then a hybrid differential evolution operator with adaptive scaling fac-tor is proposed to balance global exploration and local exploitation.Different from the existing algorithms,variable population size and dynamically increas-ing weight vector can avoid the decrease of population diversity and premature convergence.2.Multi-objective grey wolf optimizer with preference order ranking is pro-posed to select promising non-dominated solutions as the wolves leader to ef-fectively improve the hunting efficiency of wolves.The proposed algorithm in-troduces preference order into multi-objective grey wolf optimizer to identify ef-fectively the promising non-dominated solutions as leader of wolves,which can guide the hunting efficiency of wolves and improve the evolutionary process.Numerical experimental results show that the proposed algorithm has better convergence speed and accuracy.