Central Force Ooptimization For Multi-objective Optimization Problem

Recently, The multi-objective optimization problem is a research topic which isextremely important. Different from single objective optimization, multiple objectivefunctions are interrelately constrained, Therefore, in order to achieve the overallobjective of the optimization, each target needs to be considered sub-goals should becarefully dealt with.1The chapter introduced a new algorithm is proposed for the multi-objectiveoptimization problems based on the law of gravity. First, the solution of decisionvariable space is regarded as the individual with quality. Then according to the law ofgravity, we calculate total acceleration exerted on each individual, and according to thelaw of motion, a new iteration format is given. The experimental results show that thealgorithm is feasible and effective.2The chapter presents an improved multi-objective evolutionary algorithm, and thisalgorithm uses the possibility degree matrix method to define fitness function. andoffers fitness nature of non-dominate, similar with the ideas above, on the basis of thelaw of gravity, and the use of kinematics law construct a new algorithm, according toNSGA-II crowding distance method to select a certain number of non-disposableliberation into a set of J. making elements of J with a certain uniformity, and thedirection of structure be befits front search towards the best side. At last we use five testfunctions to test the algorithm, and compare the results of numerical with the other fiveclassic algorithms, The numerical results show that the algorithm is feasible andeffective.