Co-evolving bee colonies by forager migration: A multi-swarm based Artificial Bee Colony algorithm for global search space

Biswas, Subhodip; Das, Swagatam; Debchoudhury, Shantanab; Kundu, Souvik

doi:10.1016/j.amc.2013.12.023

Cited by 42 publications

(23 citation statements)

References 22 publications

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“…The above modifications help to maintain the balance between exploration and exploitation capabilities of ABC. B i s w a s et al [128] proposed a modified algorithm MiMSABC to ensure effective exploration and exploitation in multi-swarm populations. Initially groups of foragers having their perturbation schemes were taken.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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“…Using the operators which are inspired by [7] for local search, the operations can be actually wasted most of the time, because a single searching process can neither create a better solution nor generate the best solution it can possibly search. For the first objective, we propose a new searching method, which aims to find the optimal solution in the searching space of an operator.…”

Section: Half-stochastic Optimal Searching Operatorsmentioning

confidence: 99%

Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

Xue

Wang

Sheng

2016

MATEC Web of Conferences

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Abstract:The double evolutional artificial bee colony algorithm (DEABC) is proposed for solving the single depot multiple traveling salesman problem (MTSP). The proposed DEABC algorithm, which takes advantage of the strength of the upgraded operators, is characterized by its guidance in exploitation search and diversity in exploration search. The double evolutional process for exploitation search is composed of two phases of half stochastic optimal search, and the diversity generating operator for exploration search is used for solutions which cannot be improved after limited times. The computational results demonstrated the superiority of our algorithm over previous state-ofthe-art methods.

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“…However, the ABC optimization algorithm is good at exploration but poor at exploitation. In addition, its convergence speed is slow in some cases [36], [37]. Therefore, to further improve the performance of ABC optimization, some modified ABC optimizations inspired by using the Grenade explosion method [38], [39] and the Bisection explosion method [40], [41] were proposed.…”

Section: Introductionmentioning

confidence: 99%

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

Lin¹

2016

Journal of Power Electronics

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Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

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Co-evolving bee colonies by forager migration: A multi-swarm based Artificial Bee Colony algorithm for global search space

Cited by 42 publications

References 22 publications

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

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