Emerging Biology-based CI Algorithms

Xing, Bo; Gao, Wenjing

doi:10.1007/978-3-319-03404-1_17

Cited by 4 publications

(2 citation statements)

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“…Algorithms inspired by collective biologic behavior of bee, glowworm, fish school, firefly, cuckoo, krill herd [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] and collective artificial system are proposed [17] [18] [19]. See [20] for a detailed review of recently developed computational intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Framework for Fireworks Algorithm

Zheng

Janecek

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that ( i) the current selection strategy has the drawback that the contribution of the firework with the best fitness (denoted as core firework) overwhelms the contributions of all other fireworks (non-core fireworks) in the explosion operator, ( ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which significantly improves the exploitation capability by using an independent selection method and also increases the exploration capability by incorporating a crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions indicate that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, and the standard particle swarm optimization SPSO2007/SPSO2011 in terms of convergence performance.

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Section: Introductionmentioning

confidence: 99%

A Cooperative Framework for Fireworks Algorithm

Zheng

Janecek

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Within population-based metaheuristics, natureinspired algorithms based on animal collective behaviour and swarm intelligence have been widely used for solving complex optimization problems (see Parpinelli and Lopes [2], Bo and Gao [3]). In this regard, algorithms inspired by the living and the travelling of animals have received wide attention from the research community.…”

Section: Introductionmentioning

confidence: 99%

Multi-leader migrating birds optimisation: a novel nature-inspired metaheuristic for combinatorial problems

Lalla-Ruiz

Armas

Expósito‐Izquierdo

et al. 2017

IJBIC

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In this paper, we present Multi-Leader Migrating Birds Optimization. This algorithm is a nature-inspired population-based approach that exploits the concepts of selforganization, cooperation, and distribution of migrating birds. For this purpose, the individuals maintain a well-defined relationship scheme. In this regard, each individual modifies a solution of the problem at hand through a set of operators defined by the user. The individuals cooperate among themselves during the search process by sharing information about the explored search space. Moreover, we assess the performance of our algorithm on the Quadratic Assignment Problem due to the large number and heterogeneous characteristics of its application fields. The computational results disclose that the algorithm is highly competitive and provides new best solutions for the problem applied to Printed Circuit Board Problem.

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