An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

Abstract

As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems.

Hybrid Red Fox and Improved Whale Optimization Algorithm-based Clustering scheme for improving network longevity in Wireless Sensor Networks

“…In specific, the network lifetime of the proposed HRFIWOA scheme was realized to be maintained on par with the competitive approaches, since it selected only CHs depending on the exhaustive factors of distance and energy. Hence, the packet delivery rate attained by the proposed HRFIWOA scheme with different sensor nodes in the network is maximized by 15 10 and 11 demonstrates the energy consumptions and packet delay incurred by the proposed HRFIWOA and, the competitive SSMOECS, BFOFOACS, WGWOCS and SFOACS schemes with different sensor nodes in the network. The energy consumption in the network is prevented in two aspects, i) it adopted factors of energy into account during the process of clustering with CH selection, and the formulation of fitness factors during the sink mobility process in which optimal deployments are identified, and ii) it adaptively explored and exploited the network region with maximized diversity to prevent worst sensor nodes from being selected as CH.…”

“…However, the utilization of single swarm-intelligent metaheuristic algorithm for CH selection-based clustering process possesses the issue of delayed convergence and point of local optimality. Thus, hybrid swarm-intelligent metaheuristic algorithm that can balance the rate of exploration and exploitation in a predominant manner is identified to the best option for acheieving potential CH selection-based clustering process which attributes towards better energy stability and network lifetime [15].…”

“…Though WOA outdoes several meta-heuristic algorithms like Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC), it has some defects of reduced precision as well as early convergence, particularly when resolving largescale optimization problems [27]. To deal with the shortcomings, an enhanced WOA is propounded by building non-linear factors and presenting other search approaches.…”

“…In addition, 'X W ' with dimension 'D * LF' executes WOA, while 'X C ' with dimension 'D * (1 − LF)' is modified by CSO. Moreover, the update formulae of CSO with respect to horizontal and vertical crossover is depicted from Equations (25)(26)(27). : Horizontal Crossover:…”

Application of Heuristic Algorithms in the Tomography Problem for Pre-Mining Anomaly Detection in Coal Seams

“…More about the algorithm and its applications can be found in the papers [ 35 , 36 ]. Based on the above rules, we now present the pseudocode Algorithm 3 of the WOA.…”