An Improved Cockroach Swarm Optimization

Abstract

Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms.

Optimal Power Flow using Cockroach Swarm Optimization

“…Another direction may be considering improved versions of CSO [43,44], or parameter optimization [41].…”

Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

“…Obagbuwa and Adewumi improved on the efficiency of CSO by the incorporation of a new component called hunger behaviour [10]. See [6,10] for details on CSO models. Two cockroach dispersion models were described in [6,24] in the literature.…”

“…In addition, he emphasized that the general optimization terminologies, such as "solution", should be strictly used to describe any new metaheuristic [23]. The components of cockroach optimization metaheuristics are clearly described in the previous works in the literature, which include [5,6,9,10,24]. These components are constructed based on the inspiration from the social habits of natural cockroaches; each component imitates a cockroach habit; which can be adapted to solve optimization problems.…”

“…The statistic 0.8 is of a very large magnitude effect size, which indicates that the BCSO algorithm performance is significantly different from the BPSO algorithm. Additionally, in this work, the performances of the original CSO [5] and ICSO [10] were evaluated on the same set of benchmark problems and the same experimental settings as shown in this section and then was compared to the proposed binary version BCSO. Table 6 shows the simulation results of the original CSO, and Table 7 depicts the result of ICSO.…”

Discrete Multi-Valued Search Space Algorithm Based on Cockroach Swarms

“…Many real world optimization problems today such as signal design, scheduling, and power management have discrete multivalued variables. Swarm intelligence (SI) techniques are population based algorithms that are inspired by the social behaviour of animal, and one of the recent SI techniques is the cockroach swarm optimization (CSO) that imitate cockroach behaviours [6,7,8,9].…”