Drosophila Food-Search Optimization

Das, Kedar Nath; Singh, Tapan Kumar

doi:10.1016/j.amc.2014.01.040

Cited by 35 publications

(30 citation statements)

References 25 publications

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“…Also, they could be modified to be more proficient and better suited for the problem of IIR identification. In addition, several new evolutionary algorithms like Brain Storm Optimization (Shi, 2011), Fireworks Algorithm (Tan and Zhu, 2010), Virus Colony Search (Li et al, 2016), Lion Optimization Algorithm (Yazdani and Jolai, 2016), Forest Optimization Algorithm Optimization Algorithm (Mirjalili and Lewis, 2016), Sine Cosine Algorithm (Mirjalili, 2016), Drosophila Food-Search Algorithm (Das and Singh, 2014), Sperm Whale Algorithm (Ebrahimi and Khamehchi, 2016), Moth-Flame Optimization Algorithm (Mirjalili, 2015) and Water Cycle Algorithm (Eskandar et al, 2012) has been reported to provide good performance in other fields.…”

Section: Iir System Identification and Filter Designmentioning

confidence: 96%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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Section: Iir System Identification and Filter Designmentioning

confidence: 96%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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“…TLBO is modeled the educational relation between students and teacher in the classroom, this method and its modified versions also is utilizes as the optimizer tool in many different works [3,5,13,23,27,28]. DSO method models the food search strategies of the insect with the same name to search the problem domain [4]. This method uses two different patterns to globally and locally search the domain.…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of five metaheuristic methods on distinct problems

Mortazavi¹

2019

DÜMF Mühendislik Dergisi

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Metaheuristic algorithms belong to the non-gradient based optimization methods. Accomplished studies in this area reveal that each of these methods mostly has its own affirmative and inconvenient aspects. So that, one might provide a high level of exploration while the other can perform a great level of exploitation. Thus, selecting the proper and efficient algorithm for a problem can highly affect both the convergence rate and the accuracy level. There are several different metaheuristic algorithms have been announced in the technical literature in the last decade. Therefore, performing an objective comparative assessment over some of these methods can provide a fundamental and fair attitude for researchers either to select an algorithm which is more fitted with their target(s) or to develop even more efficient methods. So, the current investigation deals with evaluating and comparing of five different metaheuristic techniques emerged from ten years ago up to now. The selected methods can be sorted chronologically as Firefly Algorithm (FA), Teaching and Learning Based Algorithm (TLBO), Drosophila Food Search (DSO) method, Ions Motion Optimization (IMO) and Butterfly Optimization Algorithm (BOA). Different properties of these algorithms as convergence rate, diversity variation, complexity and accuracy level of the final solutions are compared on both constrained and non-constrained optimization problems include mathematical functions, mechanical and structural problems. The results show that the cited methods show different performance depending on the type of the optimization problem but overally BOA and TLBO outperform the other algorithms on non-constrained and constrained problems, respectively.

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“…The mathematical models of these two problems are given in Appendix-3. The minimum objective function values for RP1 and RP2 are reported in Tables 5 and Table 6, respectively, along with results quoted in (Das & Singh, 2014). The performance results are compared with DE, GSA, DE-DSA, DFO, DFO (QA) and Beightler and Phillips (1976).…”

Section: Tablementioning

confidence: 99%