Firefly algorithm with neighborhood attraction

Wang, Hui; Wang, Wenjun; Zhou, Xinyu; Sun, Haoyu; Zhao, Jia; Yu, Xiang; Cui, Zhihua

doi:10.1016/j.ins.2016.12.024

Cited by 279 publications

(147 citation statements)

References 37 publications

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“…31. Experiments on standard benchmark functions showed that the VSSFA and WSSFA reached better solutions than the standard FA on some test functions 30 17 . In this approach, each firefly was attracted by other brighter fireflies selected from a limited neighbourhood.…”

Section: Brief Review Of Famentioning

confidence: 99%

“…After their invention, metaheuristics have been modified or hybridized with other methods in order to make their performances more successful 9,10,11,12 . Specially, there are a lot of improved variants of some prominent metaheuristic algorithms proposed for global optimization 13,14,15,16,17,18 . However, studies show that it is not possible to develop a general purpose algorithm that reaches optimal solution for all types of optimization problems 19 .…”

Section: Introductionmentioning

confidence: 99%

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An improved chaotic firefly algorithm for global numerical optimization

Brajević¹,

Stanimirović²

2018

IJCIS

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Firefly algorithm (FA) is a prominent metaheuristc technique. It has been widely studied and hence there are a lot of modified FA variants proposed to solve hard optimization problems from various areas. In this paper an improved chaotic firefly algorithm (ICFA) is proposed for solving global optimization problems. The ICFA uses firefly algorithm with chaos (CFA) as the parent algorithm since it replaces the attractiveness coefficient by the outputs of the chaotic map. The enhancement of the proposed approach involves introducing a novel search strategy which is able to obtain a good ratio between exploration and exploitation abilities of the algorithm. The impact of the introduced search operator on the performance of the ICFA is evaluated. Experiments are conducted on nineteen well-known benchmark functions. Results reveal that the ICFA is able to significantly improve the performance of the standard FA, CFA and four other recently proposed FA variants.

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Section: Brief Review Of Famentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved chaotic firefly algorithm for global numerical optimization

Brajević¹,

Stanimirović²

2018

IJCIS

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“…To validate the performance of the proposed Dynamic Reduction strategy for dimension perturbation, twelve classical benchmark functions are employed. These functions were used in many optimization papers [21,[33][34][35]. Table 2 lists the function names, search range, dimension size, and global optimum.…”

Section: Benchmark Functionsmentioning

confidence: 99%

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Zhou

Wang

2019

Mathematical Problems in Engineering

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To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.

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“…In Algorithm 3 [28], the FA starts to optimize the AND-based fuzzy rules through generating randomly the population of N fireflies. Since there are 25 controllable parameters in fuzzy rules as mentioned previously, the length of feasible solution is a string of 25.…”

Section: Optimization Of And-based Fuzzy Rule Via Firefly Algorithmmentioning

confidence: 99%

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Misbahuddin

Ratna

Sari

2018

INT J COMPUT COMMUN

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In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.

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Firefly algorithm with neighborhood attraction

Cited by 279 publications

References 37 publications

An improved chaotic firefly algorithm for global numerical optimization

An improved chaotic firefly algorithm for global numerical optimization

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

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