A Method of Dynamic Multi-Objective Optimization Based on Evolutionary Mechanism

Dynamic multi-objective optimization is a new area of evolutionary computation.A method solving a class of dynamic multi-objective optimization problem(DMOP)which defined in the discrete time space and the dimension of decision variable changing with time(environment)is given.First,the DMOP is transformed into a series of homogeneous static constraint optimization problems.Then,a new dynamic multi-objective evolutionary algorithm(DMEA)is proposed based on a rule which can automatically check out the environment variation.At last,the experimental results and comparison illustrate the proposed algorithm can track the Pareto optimal solutions of DMOP and find a sufficient number of uniformly distributed Pareto optimal solutions in difference environment.