A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem

Pravesjit, Sakkayaphop

doi:10.1007/s10015-015-0248-3

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…In ( Pravesjit, 2016 ), the BA algorithm was developed by embedding the reproduction step of the Genetic Algorithm (GA) to clone each agent of the BA algorithm. Also, PSO was merged with GA ( Garg, 2016 ) where the mutation and crossover operators of GA were embedded into the PSO update procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

Issa

Helmi

Elsheikh

et al. 2022

Expert Systems with Applications

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Section: Literature Reviewmentioning

confidence: 99%

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

Issa

Helmi

Elsheikh

et al. 2022

Expert Systems with Applications

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“…NABA better than the standard Bat Algorithm for high dimensional optimization problems 2014, [70] 18 unimodal, multimodal, and continuous benchmark test functions. More efficiency and less computational time than DE, saDE, and others 2016, [71] Discrete Optimization Reducing the cost of the early and late penalty of scheduling problem of a multi-step production line.…”

Section: Optimization Problems Solved By Bat Algorithmmentioning

confidence: 99%

Critical analysis: bat algorithm-based investigation and application on several domains

Umar

Rashid

2021

WJE

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Purpose The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms. Design/methodology/approach Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm). Findings Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

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“…-Can easily to fall into the local optimum (Li and Zhou 2014;Pravesjit 2016). -Premature convergence (Ahmadi and Nikravesh 2016).…”

Section: A Comparison Of the Proposed Population-based Metaheuristicsmentioning

confidence: 99%

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

Chansombat

Musikapun

Pongcharoen

et al. 2018

International Journal of Production Research

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Capital goods companies produce high value products such as power plant or ships, which have deep and complex product structures, with components having long process routings. Contracts usually include substantial penalties for late delivery. The high value of items can lead to substantial holding costs. Efficient schedules minimise earliness and tardiness costs and need to satisfy assembly and operation precedence constraints as well as finite capacity. This paper presents the first advanced planning and scheduling (APS) tool for the capital goods industry that uses a Discrete Bat Algorithm (DBA), modified DBA (MDBA) and hybrid DBA with Krill Herd algorithm (HDBK) to optimise schedules. The tool was validated using four datasets obtained from a collaborating capital goods company. A sequential experimental strategy was adopted. The first experiment identified appropriate parameter settings for the DBA. The second experiment evaluated and compared the performance of the proposed HDBK algorithm with an Artificial Bee Colony, Krill Herd (KH), Modified KH, DBA and MDBA metaheuristics. The experimental results revealed that the HDBK performed best in terms of the minimum penalty cost for all problem sizes and achieved up to a 47.837% reduction in mean total penalty costs of extra-large problem size.

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A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem

Cited by 12 publications

References 11 publications

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

Critical analysis: bat algorithm-based investigation and application on several domains

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

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