Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Abstract

In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed. First, the standard WOA is improved from the three aspects of initial population, convergence factor, and mutation operation. At the same time, Gaussian mutation is introduced. Then the nonfixed penalty function method is used to transform the constrained problem into an unconstrained problem. Finally, 13 benchmark problems were used to test the feasibility and effectiveness of the proposed method. Numerical results show that the proposed IWOA has obvious advantages such as stronger global search ability, better stability, faster convergence speed, and higher convergence accuracy; it can be used to effectively solve complex constrained optimization problems.

Maximum power point tracking in a solar PV system: Current trends towards nature‐inspired optimization techniques

“…But, like DE, PSO, ACO, and other swarm intelligence optimization algorithms, they all share the drawbacks of sluggish convergence and easy fall into local optimum. As a result, several enhancements to the conventional algorithms have been implemented in actual applications 52 . However, DE has good capability in finding an optimal location in the defined region, which helps in reducing the number of iterations.…”

“…Realizing the advantage, several researchers have employed this technique to improve the MPP tracking capability of PV generation under PSC. In recent years, other nature-inspired optimization algorithms are gaining attention from researchers as an alternative to swarm methods, such as Ant colony optimization (ACO), [29][30][31][32] Grey wolf optimization (GWO), [33][34][35][36][37][38][39] Firefly algorithm (FFA), [40][41][42][43][44][45] Cuckoo search (CS), 46 Jaya algorithm, [47][48][49] whale optimization algorithm (WOA), [50][51][52][53] flower pollination algorithm (FPA), 54,55 artificial bee colony (ABC) algorithm, 56 Salp Swarm Algorithm (SSA), 57,58 Grasshopper Optimization Algorithm (GOA), 59,60 and Harris Hawk Optimization (HHO). 61,62 This paper focuses on techniques appearing in literature in the recent past.…”

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

“…WOA has shown high exploration ability. Unlike other meta-heuristic algorithms, WOA updates the position vector of a whale (solution) in the exploration stage with respect to the position vector of a randomly chosen search agent rather than the optimal search agent discovered so far [17,[34][35][36]. Like other meta-heuristic algorithms, WOA has drawbacks like early convergence and the ease of falling into the local optimum.…”

“…Mafarja and Mirjalili [17] combined WOA with simulated annealing (SA) algorithm to enhance its exploitation ability and applied their enhanced WOA-based approach for feature selection. Also, Ning and Cao [36] proposed an improved variant of WOA and applied it for solving complex constrained optimization problems. A Mixed-Strategy-based WOA was proposed by Ding et al [37] for optimizing the parameter of a hydraulic turbine governing system (HTGS).…”

Novel Metaheuristic Based on Iterated Constructive Stochastic Heuristic: Dhouib-Matrix-3 (DM3)

“…In 2016, the whale optimization algorithm (WOA) was firstly introduced in [11], and it is based on the behavior of whales in nature. An improved version of WOA was depicted in [12].…”