A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems

Aydilek, İbrahim Berkan

doi:10.1016/j.asoc.2018.02.025

Cited by 331 publications

(131 citation statements)

References 41 publications

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“…Algorithms include particle such as PSO, ACO, Gray wolf optimization (GWO), artificial bee colony (ABC), owl optimization algorithm (OOA), Falcon optimization algorithm (FOA), cuckoo search algorithm (CSA), and firefly algorithm (FA). Many researchers around the world have been benefited from the diversity in swarm-based algorithms, which are applied to solve complex optimization problems in various fields such as test scheduling problems, 23,24 engineering optimization problems, 10,11,[25][26][27][28] heat exchangers problems, [29][30][31] neural network parameter optimization, 32,33 health-care, 34,35 real-time object tracking, 36,37 protein detection, 38,39 task scheduling in cloud computing, 40,41 and clustering for wireless sensor networks. 42,43 The third category of algorithms that use physical or chemical systems, typically simulate physical phenomena occurring in nature like Newton's gravitational law, quantum mechanics, and universe theory.…”

Section: Literature Reviewmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Section: Literature Reviewmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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“…With a decrease in light intensity, it is absorbed in the media letting the attractiveness vary with absorption. As per the inverse square law [20], light intensity (I (r)) at distance r from the light source (l s ) may be computed as per equation (1)…”

Section: B Fire Fly (Ff) Algorithmmentioning

confidence: 99%

Hybrid Differential Evolution Based Probabilistic Model to Support Quality Aware Best Path Routing Scheme for Mobile Adhoc Networks

2020

IJRTE

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 Abstract: The Mobile Ad hoc Networks (MANETs) today represent a new system containing wireless mobile nodes to dynamically and freely organize the topologies of the network without having any communication infrastructure. Owing to the traits such as temporary topology and the absence of a proper centralized authority routing can be a very important issue faced by the ad hoc networks. This multipath routing algorithm has established various paths between the source node and its destination node thus spreading traffic load along various routes. This will be able to alleviate congestion of traffic on a particular path. Thus, the multipath routing algorithms were able to provide route resilience that ensured data transmission reliability. For the purpose of this work, there was a multipath routing scheme which is called the Ad hoc On-demand Multipath Distance Vector Routing (AOMDV) Protocol. This was proposed by means of employing a hybrid Fire Fly (FF) with Differential Evolution (DE) algorithm. This approach was able to achieve better, as well as, reciprocal advantages in a dynamic and hostile network situation. Thus, the proposed scheme of routing was a very powerful method to find effective solutions to MANET routing problems. The proposed method is a well-known probabilistic metaheuristic algorithm to improve the quality aware best path routing protocol. Simulation results indicate that the proposed method FF-DE achieves better performance than AOMDV and FF.

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“…Tables 21 and 22 indicate that the proposed AUFPSO-NTE had a better variance in the optimum compared to AFO-DPSO, NCPSO, FO-DPSO, FPSO, APSO, DPSO, HPSO, and PSO. Example (4) compares the performance of the proposed AUFPSO-NTE with the HAFPSO (hunter-attack fractional-order PSO) developed by Hosseini et al [38], GAPSO (genetic algorithm-PSO) [59], HFPSO (hybrid firefly algorithm and PSO) [60], FPSO, and PSO developed by Hosseini et al [38]. The HAFPSO introduces the concept of hunter-attack into the FODPSO and the GAPSO is a compound optimizer that introduces the crossover and mutation strategy of GA into PSO.…”

Section: Aufpso-nte Fvfp-pso Fp-pso Fv-pso Psomentioning

confidence: 99%

Adaptive-Uniform-Experimental-Design-Based Fractional-Order Particle Swarm Optimizer with Non-Linear Time-Varying Evolution

et al. 2019

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An adaptive-uniform-experimental-design-based fractional particle swarm optimizer (AUFPSO) with non-linear time-varying evolution (NTE) is proposed. A particle swarm optimizer (PSO) is an excellent evolutionary algorithm due to its simple structure and rapid convergence. Nevertheless, PSO has notable drawbacks. Although many proposed methods and strategies have enhanced its effectiveness and performance, PSO is limited by its tendency to fall into local optima and its tendency to obtain different solutions in each search (i.e., its weak robustness). Introducing fractional-order calculus in PSO (FPSO) can correct the order of the velocity derivative for each particle, which enhances the diversity and algorithmic effectiveness. This study used NTE of the order of the velocity derivative, inertia weight, cognitive parameter, and social parameter in an FPSO used to search for a global optimal solution. To obtain the best combination of FPSO and NTE, an adaptive uniform experimental design (AUED) method was used to deal with this essential issue. The AUED method integrates a uniform layout with the best combination phase and a stepwise ratio to assist in selecting the best combination for FPSO-NTE. Experimental applications in 15 global numerical optimization problems confirmed that the AUFPSO-NTE had a better performance and robustness than existing PSO-related algorithms.

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A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems

Cited by 331 publications

References 41 publications

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Hybrid Differential Evolution Based Probabilistic Model to Support Quality Aware Best Path Routing Scheme for Mobile Adhoc Networks

Adaptive-Uniform-Experimental-Design-Based Fractional-Order Particle Swarm Optimizer with Non-Linear Time-Varying Evolution

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