Abstract
The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO) is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP), which is considered to be an NP-hard Combinatorial Optimization Problem (COP). A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO) algorithm on TSP were compared to other meta-heuristic algorithms.
Highlights
Evolutionary Computation (EC) encompasses all population-based algorithms
Examples of Evolutionary Algorithms (EA) algorithms includes Genetic Algorithm (GA), which models genetic evolution; Genetic Programming (GP), which is based on GA, but with individual programs represented as trees; Evolutionary Programming (EP), which is derived from the simulation of adaptive behavior in evolution; Evolutionary Strategies (ES), which are geared towards modeling the strategic parameters that control variation in evolution; and Differential Evolution (DE), which is similar to GA, but differs in the reproduction mechanisms used
This paper presented the Binary Cockroach Swarm Optimization (BCSO) algorithm
Summary
Evolutionary Computation (EC) encompasses all population-based algorithms. Algorithms are constructed on a set of multiple solution candidates (which are referred to as a population) and are iteratively refined [1]. Wu and Wu proposed a cockroach genetic algorithm for estimating the parameters of the biological system in showing a net interactive effect of systems biology (S-system) where cockroach behavior was imitated and embedded into an advanced genetic algorithm to increase exploration and exploitation abilities [11] Many optimization problems, such as sensor management, routing and scheduling, have their decision variables in a binary format [12]. He stated further that the novelty of the original metaphor does not inevitably guarantee the novelty of the resulting framework He pointed out that high quality research in metaheuristics is expected to be framed effectively in the general literature of metaheuristics and optimization, which should involve deconstruction, that is, showing the components it is made up of and the adaptation of the components to a particular problem being solved [23]. The remaining part of the paper is as follows: Section 2 explains CSO and the motivation for the development of its binary version; Section 3 shows the proposed BCSO algorithm models; Section 3 describes the TSP approach; Section 4 presents the set of experiments conducted to evaluate the proposed BCSO algorithm, with the respective results and comparative results; and Section 5 summarises the paper
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