Tackling global optimization problems with a novel algorithm – Mouth Brooding Fish algorithm

Jahani, Ehsan; Chizari, M.

doi:10.1016/j.asoc.2017.09.035

Cited by 73 publications

(29 citation statements)

References 34 publications

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“…The individual in GS is threatened if the distance between itself and the predator is shorter than the hunting radius. All the threatened individuals 'escape' by Formula (16) and continue searching around the new position by Formula (17) after 'escaping'.…”

Section: Motivationmentioning

confidence: 99%

“…The mouth brooding fish algorithm simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. The proposed algorithm uses the movement, dispersion and protection behavior of a mouth brooding fish as an update mode, and the individuals in the algorithm are updated after these three stages to find the best possible answer [16].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Squirrel Search Algorithm for Global Function Optimization

Wang

2019

Algorithms

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An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Squirrel Search Algorithm for Global Function Optimization

Wang

2019

Algorithms

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“…It simulates a collision between two colliding bodies. The mouth-brooding fish algorithm (MBF) by Jahani and Chizari (2018) is also a recently developed population-based meta-heuristic algorithm that is inspired by the life-cycle of mouth-brooding fish.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Algorithm for Cost Optimization of Waffle Slab

Shayegan

Lork

Hashemi

2020

Slovak Journal of Civil Engineering

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In this paper, we have developed an efficient hybrid meta-heuristic algorithm for structural cost optimization of a waffle slab and have also solved the relevant optimization problems. The cost of the waffle slab is considered to be the objective function, and the design is based on the American Concrete Institute’s ACI 318-08 standard. This algorithm utilizes the recently developed mouth-brooding fish (MBF) algorithm as the main engine and uses the favorable properties of the colliding bodies optimization (CBO) algorithm. The performance of this algorithm is compared with MBF, CBO, harmony search (HS), particle swarm optimization (PSO), democratic particle swarm optimization (DPSO), charged system search (CSS) and enhanced charged system search (ECSS). The numerical results demonstrate that the proposed algorithm can construct promising results and has merits in solving challenging optimization problems.

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“…Some other popular swarm intelligence algorithms are firefly mating algorithm (FMA) [9], shuffled frog leaping algorithm (SFLA) [10], bee collecting pollen algorithm (BCPA) [11], cuckoo search (CS) algorithm [12], dolphin partner optimization (DPO) [13], bat-inspired algorithm (BA) [14], firefly algorithm (FA) [15], and hunting search (HUS) algorithm [16]. Some of the recent swarm intelligence algorithms are fruit fly optimization algorithm (FOA) [17], dragonfly algorithm (DA) [18], artificial algae algorithm (AAA) [19], ant lion optimizer (ALO) [20], shark smell optimization algorithm (DSOA) [21], whale optimization algorithm (WOA) [22], crow search algorithm (CSA) [23], grasshopper optimization algorithm (GOA) [24], mouth brooding fish algorithm (MBFA) [25], spotted hyena optimizer (SHO) [26], butterfly-inspired algorithm (BFA) [27], squirrel search algorithm (SSA) [28], Andean condor algorithm (ACA) [29], and pity beetle algorithm (PBA) [30].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

Zheng

Luo

2019

Computational Intelligence and Neuroscience

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Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.

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Tackling global optimization problems with a novel algorithm – Mouth Brooding Fish algorithm

Cited by 73 publications

References 34 publications

An Improved Squirrel Search Algorithm for Global Function Optimization

An Improved Squirrel Search Algorithm for Global Function Optimization

A New Hybrid Algorithm for Cost Optimization of Waffle Slab

An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

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