A Guided Differential Evolutionary Multi-Tasking with Powell Search Method for Solving Multi-Objective Continuous Optimization

Recent years, the field of Multi-Objective Optimization (MOO) has attracted remarkable consideration among evolutionary computation researchers. Evolutionary multitasking paradigm within the domain of MOO has been proposed and demonstrated on some benchmark test functions that indicates potential applications in real world problems. The concept of evolutionary multi-tasking is founded on the fact that individuals from various cultures may share their underlying similarities, thereby facilitating improved convergence characteristics. However, the designate algorithm for MOO multi-tasking is originated from pure genetic search that means it does not imply any advanced local refinement method which also improves the rate of convergence. Memetic algorithms, which is known as a synergy of evolutionary with separate individual learning or local improvement procedures for problem search, offers converging to high-quality solutions more efficiently than their conventional evolutionary counterparts. Accordingly, in this paper, to excel MOO multi-tasking paradigm performance, we propose an algorithm which is based on the idea of Multi-Factorial Evolutionary Algorithm (MFEA) employing Guided differential evolutionary and Powell local search. The accomplished experimental results point out using memetic techniques does an impressive enhancement on Multi-objective continuous optimization.

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