A dynamic multi-objective evolutionary algorithm based on intensity of environmental change - Hu, Yaru; Zheng, Jinhua; Zou, Juan; Yang, Shengxiang; Ou, Junwei; Wang, Rui (Information sciences, 2020)
•This paper introduced U-test mechanism to test decision variable and market them into macro-changing decision and micro-changing decision.•An effective update mechanism based on historical information was proposed to improve the convergence of population.•Two different parts including macro-changing decision and micro-changing decision were implemented to produce better solutions.•The statistical results show that the proposed algorithm is very competitive in terms of convergence and diversity as well as the response speed to changes, when compared with four other state-of-the-art methods. This paper proposes a novel evolutionary algorithm based on the intensity of environmental change (IEC) to effectively track the moving Pareto-optimal front (POF) or Pareto-optimal set (POS) in dynamic optimization. The IEC divides each individual into two parts according to the evolutionary information feedback from the POS in the current and former evolutionary environment when an environmental change is detected. Two parts, the micro-changing decision and macro-changing decision, are implemented upon different situations of decision components in order to build an efficient information exchange among dynamic environments. In addition, in our algorithm, if a new evolutionary environment is similar to its historical evolutionary environment, the history information will be used for reference to guide the search towards promising decision regions. In order to verify the availability of our idea, the IEC has been extensively compared with four state-of-the-art algorithms over a range of test suites with different features and difficulties. Experimental results show that the proposed IEC is promising.