Selective Pressure Strategy in differential evolution: Exploitation improvement in solving global optimization problems

The paper proposes a modification of Differential Evolution mutation strategies with the introduction of selective pressure, which is implemented by applying proportional, rank-based and tournament selection. Based on the new mutation strategies, a new algorithm called LSHADE-SP is proposed, which is a modification of the LSHADE algorithm, with various types of selective pressure implementation. The algorithm is tested against the Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization benchmark functions to demonstrate the advantage of using selective pressure. The comparison shows that applying linear rank, exponential rank and tournament selection deliver faster convergence, if a proper selective pressure is applied. The experiments were conducted for both classical mutation strategies, like rand/1 and best/1, and the best state-ofthe art strategies, with various parameter adaptations. The results demonstrate that the algorithm with selective pressure is superior to the best state-of-the-art non-hybrid DE algorithms. The resulting algorithm, LSHADE-SP, obtained one of the best results among the algorithms that were winners of the CEC 2017 competition on real-parameter bound-constrained optimization.