An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

Abstract

Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

“…Sun et al [104] proposed a relatively simple and direct method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on successhistory-based adaptive differential evolution (SHADE) in high-dimensional search space. Hu and Li [105] proposed an enhancing differential evolution algorithm with a rank-up selection (RUSDE) which is applied to the real-world optimization problem of the four-bar linkages, where the performance of RUSDE is better than other algorithms. Krink and Paterlini [106] proposed a new multi-objective evolutionary algorithm based on differential evolution (DEMPO) for portfolio optimization, which can obtain very satisfying results in reasonable runtime.…”

Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

“…The DE algorithm has maintained its influence for the last three decades due to its excellent performance. Many of its variants have placed among the top ranks in the IEEE CEC conference series [16,17]. Its straight forward execution, simple and small structure, and quick convergence can be considered the main reasons for its great efficiency.…”