Single And Multi Objective Optimization Based On Particle Swarm Optimization Algorithm

As a efficient and simple evolutionary algorithm,particle swarm optimization algorithm has been applied to solve the single and multi objective optimization problems.However,when dealing with the single objective optimization problem,due to its self and global learning mechanism,particle swarm optimization has some shortcomings,such as premature convergence,easy to fall into the local optimum and so on.When dealing with multi-objective problems,there is a problem that the algorithm can not traverse the whole decision space effectively,which leads to the imbalance between the diversity and convergence of solutions.In view of these problems,the following work has been done in this dissertation.(1)Terminal crossover and steering-based PSO with distribution(TCSPSO)is proposed in this dissertation.In TCSPSO,to enhance the diversity of population,a new crossover mechanism is constructed.Meanwhile,in order to make the particle easily jump out of the local optimum,a global disturbance is utilized and the direction of motion of the particle is changed at the later stage.Finally,a nonlinear inertia weight and elastic mechanism are introduced to balance exploration and exploitation better.34 benchmark functions and two engineering problems are utilized to verify the promising performance of TCSPSO,experimental results and statistical analysis indicate that TCSPSO has competitive performance compared with 15 state-of-the-art algorithms.(2)Avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue.To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems,a modified PSO using adaptive strategy called MPSO is proposed.In MPSO,in order to well balance the global exploration and local exploitation capabilities of the PSO,a chaos based non-linear inertia weight is proposed.Meanwhile,to avoid the premature convergence,stochastic and mainstream learning strategies are adopted.Finally,an adaptive position updating strategy and terminal replacement mechanism are employed to enhance PSO's ability to solve complex optimization problems in expert systems.30 complex CEC2017 benchmark functions are utilized to verify the promising performance of MPSO,experimental results and statistical analysis indicate that MPSO has competitive performance compared with 16 state-of-the-art algorithms.The source code of MPSO is provided at https://github.com/lhustl/MPSO.(3)In the past few decades,evolutionary multi-objective optimization has become a research hotspot in the field of evolutionary computing,and a large number of multiobjective evolutionary algorithms(MOEAs)have been proposed.However,MOEA is still faced with the problem that the diversity and convergence of non-dominated solutions are difficult to balance.To address these problems,an efficient multi-objective optimization algorithm based on level swarm optimizer(EMOSO)is proposed in this dissertation.In EMOSO,a sorting method is introduced to balance the diversity and convergence of non-dominated solutions in the whole population,which is based on non-dominated relationship and density estimation.Meanwhile,a level-based learning strategy is introduced to maintain the search for non-dominated solutions.Finally,DTLZ,ZDT and WFG series problems are utilized to verify the performance of the proposed EMOSO.Experimental results and statistical analysis indicate that EMOSO has competitive performance compared with 6 popular MOEAs.The source code of EMOSO is provided at:https://github.co m/xuweizhang163/EMOSO.(4)In this dissertation,a modified particle swarm optimization(AMPSO)is proposed to solve the multimodal multi-objective problems.Firstly,a dynamic neighborhoodbased learning strategy is introduced to replace the global learning strategy,which enhances the diversity of the population.Meanwhile,to enhance the performance of PSO,the offering competition mechanism is utilized.11 multimodal multi-objective optimization functions are utilized to verify the feasibility and effectiveness of the proposed AMPSO.Experimental results and statistical analysis indicate that AMPSO has competitive performance compared with 5 state-of-the-art multimodal multi-objective algorithms.