An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Abstract

Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.

An Enhanced Neural Network Algorithm with Quasi-Oppositional-Based and Chaotic Sine-Cosine Learning Strategies

“…Hence, it can be summarized that the proposed QOCSCNNA demonstrates superior feasibility compared to other algorithms. This problem is being solved by several researchers using different methods, such as NNA, WOA, SCA, SA and PSO used in [42]. Table 2 re optimal result of QOCSCNNA is 1.3548, as well as the optimal constraints o QOCSCNNA, satisfy Equation ( 19), which proves the validity of the opti obtained by QOCSCNNA.…”

“…This problem is being solved by several researchers using different metaheuristic methods, such as NNA, WOA, SCA, SA and PSO used in [ 42 ]. Table 2 reveals that the optimal result of QOCSCNNA is 1.3548, as well as the optimal constraints obtained from QOCSCNNA, satisfy Equation (19), which proves the validity of the optimal solutions obtained by QOCSCNNA.…”

Unmanned Aerial Vehicle 3D Path Planning Based on an Improved Artificial Fish Swarm Algorithm

“…Table 1. The CEC2020 benchmark function [46]. Numerical experiments were performed under Dim = 10 conditions, and the proposed algorithm was compared with the original AFSA, PSO, IAFSA1 with probability weighting factors, IASA2 with integrated survey behavior, and IAFSA3 with an initialization strategy and adaptive strategy [47].…”

An enhanced dung beetle optimizer for cloud task schedule

“…However, these algorithms still have limitations and cannot provide good accuracy and speed when dealing with task scheduling problems. Meta-heuristic techniques using improved strategies have been proven in the literature [9][10] [11] [12] [13] to address the cloud job scheduling problem more efficiently. Therefore, this paper is motivated by the possibility of proposing a novel learning strategy-based optimization method to boost the precision and speed of solving cloud scheduling problems.…”