Beetle Colony Optimization Algorithm and its Application

Abstract

Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning.

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

“…Zhang et al, based on the principles of the ABC algorithm, combined ABC and BAS algorithms to propose the Multi-Task Beetle Antennae Swarm Algorithm (MBAS) (Zhang et al 2020b). This algorithm constructs a beetle particle swarm of size N in multi-dimensional space, dividing these beetle particles into three categories: searchers, followers, and explorers, based on a specific ratio.…”

Multi-Subdomain Grouping-Based Particle Swarm Optimization for the Traveling Salesman Problem

“…For each subdomain, a similar principle of determining the end and planned points with the foursubdomain grouping is adopted. That is, the end points are the two cities that have the maximum and minimum orientation angles to their nearest geographical center (see (11)), and the remaining cities within this subdomain are planned points. Here, it is expected that there are sufficient planned points for partial path optimization within the two middle regions.…”

“…Many popular algorithms for solving TSPs are metaheuristic types, e.g., ant colony optimization (ACO) [7], tabu search [8], genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], beetle colony optimization (BCO) [11], and social spider algorithm (SSA) [5]. They learn from the collective intelligence of various organisms and natural evolutionary law and find a nearly optimal solution within a reasonable time period.…”

Enhanced beetle antennae search algorithm for complex and unbiased optimization

“…At each temperature, SABAS searches solution space multiple times and the step size is proportional to the current temperature, thus improving the global and local search abilities of the algorithm. In a similar way, BAS has been combined with ACO or ABC to form BCO and MABC (Zhang et al 2020a , b ) algorithms, respectively. These studies proved the usability of BAS as an additional strategy to improve other intelligent algorithms.…”