Swarm Intelligence in Optimization

Blum, Christian; Li, Xiaodong

doi:10.1007/978-3-540-74089-6_2

Cited by 335 publications

(194 citation statements)

References 146 publications

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“…Swarm intelligence is based on the study of behaviour of simple individuals (e.g. ant colonies, bird flocking, and honey bees, animal herding) that mimics the behaviour of swarms of social insects or animals [6]. More and more researches are interested in this field as swarm intelligence offers new ways of designing intelligence systems.…”

Section: Swarm Intelligencementioning

confidence: 99%

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Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

al-Rifaie

2016

Studies in Computational Intelligence

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This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS) -a swarm intelligence algorithm -to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally, the algorithm is applied to overlap score matrices which are generated from the raw input fragments; the algorithm optimises the overlap score matrices to find better results. In these experiments realworld data are used and the performance of SDS is compared with several other algorithms which are used by other researchers in the field, thus demonstrating its weaknesses and strengths in the experiments presented in the paper.

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Section: Swarm Intelligencementioning

confidence: 99%

“…Among the successful examples of optimisation techniques inspired by swarm intelligence are: ant colony optimisation (inspired by foraging behaviour of real ant colonies) and particle swarm optimisation (inspired by bird flocking) [6]. In this work, Stochastic Diffusion Search (SDS) [1] algorithm is used.…”

Section: Swarm Intelligencementioning

confidence: 99%

Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

al-Rifaie

2016

Studies in Computational Intelligence

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“…Examples of local search methods that can be used are taboo search [24], simulated annealing and constraint optimization methods [31] [65]. It is also possible to apply swarm intelligence as a local optimization method [8] [9], such as ant colony optimization, particle swarm optimization and intelligent water drops.…”

Section: Schedulingmentioning

confidence: 99%

Intelligent Decision Support and Agent-Based Techniques Applied to Wood Manufacturing

Elghoneimy

Gruver

2011

Advances in Intelligent and Soft Computing

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“…Researchers are attracted with the behavior of flocks of social insects from many years. The individual member of these flocks can achieve hard tasks with the help of collaboration among others [5]; however, such colonies behave without any centralized control. SI is very famous in bioinspired algorithms, computer science, and computational intelligence.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Mutation Strategies in Bat Algorithm

Bangyal¹,

Ahmad²,

Tayyab³

et al. 2018

ijacsa

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Abstract-Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind of optimization problems and one of major issue faced by BA is frequently captured in local optima meanwhile handling the complex real world problems. Many authors improved the standard BA with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This paper aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of BA. It has been tried to provide guidelines for the researchers who are active in the area of BA and its mutation strategies. The objective of this study is divided in two sections: primarily to display the improvement of BA with mutation strategies that may enhance the performance of standard BA up to great extent and secondly, to motivate the researchers and developers for using BA to solve the complex real world problems. This study presents a comprehensive survey of the various BA algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.

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Swarm Intelligence in Optimization

Cited by 335 publications

References 146 publications

Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

Intelligent Decision Support and Agent-Based Techniques Applied to Wood Manufacturing

An Overview of Mutation Strategies in Bat Algorithm

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