An improved firefly algorithm for solving dynamic multidimensional knapsack problems

Baykasoğlu, Adil; Özsoydan, Fehmi Burçin

doi:10.1016/j.eswa.2013.11.040

Cited by 113 publications

(57 citation statements)

References 28 publications

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“…In this way, partial knowledge of the previous environment is preserved. On the same problem but with a different type of dynamic changes that does not affect the representation of the problem, Baykasoglu and Ozsoydan [137] enhanced the diversity of FA by partially restarting a part of the population when a dynamic change occurs. Montemanni et al [67] addressed the DVRP by dividing the working day into time slices.…”

Section: Increasing Diversity After a Changementioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Increasing Diversity After a Changementioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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“…In some of these algorithms, used particularly for solving the problems in the field of engineering, obtainment of the appropriate results can be seen [17][18][19]. In recent years, one can observe that algorithms such as bat and firefly algorithms, from which we inspired, are widely used for solving the problems [20][21][22][23]. Naturally, the objective of the investigators is opining what can be done in this field; in this study, we aimed to demonstrate the consequences of an approach [24] which could serve novel solutions to the optimization problems in both engineering and the other fields, by applying it on some engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Canayaz

Karcı

2015

Appl Intell

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Meta-heuristic algorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired metaheuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.

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“…al., [67] have presented a comparative analysis of the performances of GA, DE and FA on both static and dynamic multidimensional knapsack problems. One of the important contributions of their study is the development of FA2, which is designed for a more realistic reflection of the behaviors of fireflies and it requires less computational time.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

Nayak¹,

Naik²,

Behera³

2015

IJDTA

143

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An improved firefly algorithm for solving dynamic multidimensional knapsack problems

Cited by 113 publications

References 28 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges

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