Adaptive cockroach swarm algorithm

Obagbuwa, Ibidun Christiana; Abidoye, Ademola P.

doi:10.1063/1.4992537

Cited by 2 publications

(3 citation statements)

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“…In Refs. [13] and [14], ACSO and CCO have demonstrated the good performance on most of test problems. For the shifted problems with 1000 dimensions, ACSO demonstrates better performance than that of CRIO and CCO on most of test functions.…”

Section: Resultsmentioning

confidence: 99%

“…The details of evaluation criteria are described as follows: 1) Problems: all problems in Appendix A; For CRIO, ACSO and CCO, the parameter setting is employed from the relevant Refs. [12][13][14]. For the four algorithms, the population sizes are all set as 1000, which is equal to the problem size D. Training period of is CCCO set as T r = M ax − F Es/10 = 500 × D = 5.0E + 5 and the number of cockroach for wall-following search is set as W number = C number /5 = D/5 = 2.0E + 2. w + denotes the Wilcoxon signed-rank test.…”

Section: Comparison Of Ccco With the Cockroachinspired Algorithmsmentioning

confidence: 99%

“…Based on Ref. [9], Obagbuwa proposes an Adaptive cockroach swarm algorithm (ACSO) for global optimization [ 13 ] , which executes an adaptive search for each iteration. Cheng presents the Cockroach colony optimization (CCO) algorithm in Ref.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bionic Optimization Technique with Cockroach Biological Behavior

Cheng

Lyu

Song

et al. 2021

Chinese J of Electronics

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Many practical engineering problems can be abstracted as corresponding function optimization problems. During the last few decades, many bionic algorithms have been proposed for this problem. However, when optimizing for large scale problems, such as 1000 dimensions, many existing search techniques may no longer perform well. Inspired by the social model of cockroaches, this paper presents a novel search technique called Cooperation cockroach colony optimization (CCCO). In the CCCO algorithm, two kinds of special biological behavior of cockroach, wall‐following and nest‐leaving, are simulated and the whole population is divided into wall‐following and nest‐leaving populations. By the collaboration of the two populations, CCCO accomplishes the computation of global optimization. The crucial parameters of CCCO are set by the self‐adaptive method. Moreover, a discussion on group model design is provided in this paper. The CCCO algorithm is evaluated with shifted test functions (1000 dimensions). Three state‐of‐the‐art cockroach‐inspired algorithms are used for the comparative experiments. Furthermore, CCCO is applied to a real‐world optimization problem concerning spread spectrum radar poly‐phase. Experiment results show that the CCCO algorithm can be applied to optimize large‐scale problems with the good performance.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Of Ccco With the Cockroachinspired Algorithmsmentioning

confidence: 99%