Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

Abstract

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

A Novel Bat Algorithm with Asymmetrical Weighed Variational Method in the Path Planning of UAVs

“…Considering the complexity of the problem and the uncertainty of the objective function, each optimization algorithm has its inherent advantages and disadvantages, but it cannot only use a single optimization calculation to solve the current optimization problem, let alone find a satisfactory solution. In recent years, more and more scholars have focused their research on discrete and multiobjective bat algorithms and achieved the purpose of improving the feasible solution of the algorithm by introducing chaos, Levy flight, or a combination with other algorithms in terms of initializing populations, habitat selection, and control parameters [69][70][71][72][73][74][75][76]. Therefore, it is a development trend to combine the bat algorithm with other methods to solve the disadvantages of the algorithm, and its specific improvement strategy can be combined with the application problems in different fields to flexibly solve the problem according to the actual situation so as to improve the convergence speed and accuracy of the algorithm, the overall performance of the algorithm, and its ability to conduct a local and global search.…”

An Improved Particle Swarm Optimization Algorithm for Data Classification

“…OBL is used in many areas, such as agriculture for preserving water crops, soil purification, medical fields for disease diagnosis, scheduling of agricultural work [31]. It is applied in many well-known algorithms such as the Bat algorithm [32,33], PSO [34], Grey Wolf Optimizer (GWO) [35], Harmony Search (HS) [36], Differential Evolution (DE) [37] and Artificial Neural Networks (ANNs).…”