Research On And Application Of Electromagnetism-like Mechanism Algorithm For Optimization Problems

Global optimization has been widely used in many fields, and lots of optimizationproblems in actual engineering can be treated as global optimization problems, whichimproves the advance and development of global optimization techniques on a largescale. However, non-linear optimization problems are one of the most difficult problemsin optimization fields, which are rather hard to be solved with traditional optimizationmethods independently. Nature-inspired algorithms are new methods of optimizationbased on co-evolution of a group of individuals in recent years and provide new toolsfor complex optimization problems. Due to its advantages of intelligence, wideapplicability, parallelism and global search ability, nature-inspired optimizationalgorithms have become a hot research area and have been widely used in many fields.The Electromagnetism-like Mechanism(EM) method is a new heuristic algorithmwhich is inspired by the attraction-repulsion mechanism between the charged particlesin the electromagnetic field. Based on the analysis of the research on EM algorithm athome and abroad, a new EM algorithm based on Tent mapping model is proposed hereaccording to the problems in standard EM algorithm, such as non-uniform initialpopulation, low search efficiency, great impact of distance factor on total force and higherror rate of directions in the move process of particles. The Tent chaos mapping modelis used in the new algorithm, which not only makes full use of the ergodicity advantageof chaos, but also generates a more uniform initial population than Logistic mappingmodel. Based on the advantages of chaos search and steepest descent method, the newalgorithm uses both of them to improve the local search process, which can not onlyavoid the algorithm falling into a local optimum prematurely, but also improve theaccuracy of the solution. Besides, the new algorithm improves the formula of force anddesigns a new correction factor for total force, which corrects the effect of distance ontotal force. Finally, the niche technology is introduced into the move process of particlesin new algorithm, which ensures the effectiveness of particles’ moving directions andimproves the efficiency of the algorithm. Finally, by introducing the expression methodof random keys, the improved algorithm is used to solve the flow shop schedulingproblem.Experimental results show that not only the solutions obtained by the improvedalgorithm is better than the ones by the standard algorithm when solving someunconstrained optimization problems, but also the convergence rate of the new algorithm is faster. And general functions, complex functions, Dixon and Szeg functions and high-dimensional functions are included in the unconstrainedoptimization problems mentioned above. Besides, it is successful for the new algorithmto solve the flow shop scheduling problem.