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Multi-tasking optimization algorithm attracts much attention because the knowledge transfer between tasks enables the algorithm to process multiple related tasks simultaneously. However, negative knowledge transfer occasionally occurs, which may weaken the performance of the algorithm. To reduce the impact of negative knowledge transfer, a multi-objective multi-tasking optimization algorithm (IM-MFEA) based on inverse model mapping and an objective transformation strategy is proposed. First, correlation analysis is applied in an inverse mapping strategy to improve the accuracy of the inverse mapping model. Then, following the pattern of using the source domain solutions to assist the optimization of the target domain, the adaptive transformation strategy is used to improve the quality of the source domain solution in the objective space. These transformed solutions are reconstructed through the inverse mapping strategy. Finally, these reconstructed source domain solutions are mated with the target domain solutions to generate competitive offspring individuals for the target domain. To verify the effectiveness of the IM-MFEA, comprehensive experiments were conducted on nine multi-objective multi-factorial optimization (MFO) benchmark problems. Empirical results demonstrate that IM-MFEA is superior to other algorithms in 90% of test instances by inverted generational distance (IGD) and hypervolume (HV) value indicators. • An adaptive transformation strategy for scaling solutions is proposed. • An inverse mapping strategy is proposed to improve offspring individual's quality. • Algorithm IM-MFEA is proposed to reduce the impact of the difference between tasks. [ABSTRACT FROM AUTHOR]

Dynamic multi-objective optimization problems (DMOPs) which contain various Pareto-optimal front (PF) and Pareto-optimal set (PS) have gained much attention. Accurate environmental change detection reveals the change degree of DMOPs and contributes the algorithm to quickly respond to the environment changes. In order to fully detect environmental changes and efficiently track front, a double-space environmental change detection and response strategy (DSDRS) is proposed. It could detect whether the environment has changed while explore the change intensity of PF and PS, respectively. Moreover, different response strategies are implemented for PF and PS. For PF environmental changes, a multiple knee points-guided evolutionary strategy (MKGES) is proposed, which is driven by front shape information and adaptively responds to different PF change intensities. For PS environmental changes, a knowledge guided memory strategy (KGMS) is proposed, which guides population evolution based on environmental information. The effectiveness of DSDRS is confirmed by comparison with five evolutionary algorithms on 20 dynamic multiobjective benchmark functions. Simulation results demonstrate that the performance of proposed algorithm is outstanding on test functions with complex changing PF and PS. [ABSTRACT FROM AUTHOR]

Proximity and diversity are two basic issues in multi-objective optimization problems. However, it is hard to optimize them simultaneously, especially when tackling problems with complicated Pareto fronts and Pareto sets. To make a better performance of multi-objective optimization evolutionary algorithm, the environmental information and history information are used to generate better offsprings. The conception of locality and reference front is introduced to improve the diversity. Adaptation mechanism of evolutionary operator is proposed to solve searching issue during different stages in evolutionary process. Based on these improvement, an improved multi-objective evolutionary algorithm based on environmental and history information (MOEA-EHI) is presented. The performance of our proposed method is validated based inverted generation distance (IGD) and compared with three state-of-the-art algorithms on a number of unconstrained benchmark problems. Empirical results fully demonstrate the superiority of our proposed method on complicated benchmarks. [ABSTRACT FROM AUTHOR]