Research In Intelligent Optimization Algorithm Based On Multi-objective And Many-objective Problems

In engineering and scientific fields, there exist many complex optimization problems. Because of their complexity, dynamic and difficulty to modeling, conventional operational research methods cannot solve them well. However, heuristic-based intelligence algorithm has advantages of solving these optimization problems. Among them, the Artificial Immune System and the particle swarm optimization algorithm as novel intelligent algorithms have attracted more and more attention. The Artificial Immune System developed by simulating the information processing principle and mechanism of biological immune system. Particle Swarm optimization algorithm is simulated from predation processing of bird flock.In this paper, the main work is based on the two intelligent algorithms to create new algorithms for multi-objective optimization and many-objective optimization problems. Firstly, we designed a new immune algorithm for the multi-objective optimization problems. Then, we proposed a novel particle swarm optimization algorithm for many-objective optimization problems. The main works of this paper as follows:1) For multi-objective optimization problems, this paper proposes a double modules immune multi-objective optimization algorithm(DMMO). The main improvement is to propose a double modules structure and integrates the advantages of two modules. DE operator is used in module I to improve the speed of convergence, and SBX operator is used in modules to enhance the diversity of population.2) For many-objective optimization problems, this paper proposes a novel particle swarm optimization algorithm for many-objective optimization algorithm(PSOMO). In the algorithm, a new fitness assignment method is created to enhance the convergence performance of the algorithm which settles the difficult of selection pressure, and effectively removes the bad particles in the population. At the same time, evolve operators have been used in our algorithm to supplement the defect with the simple particle swarm optimization search method and enhance the convergence rate and population diversity of algorithm.