An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions

Molina, Daniel; LaTorre, Antonio; Herrera, Francisco

doi:10.1007/s12559-018-9554-0

Cited by 76 publications

(40 citation statements)

References 142 publications

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“…In this subsection, WFS will be compared with 11 similar classical global optimization algorithms, such as: Genetic Algorithm (GA) [34], Particle Swarm Optimization (PSO) [6], Bat Algorithm (BA) [58], Grey Wolf Optimizer (GWO) [33], Butterfly Optimization Algorithm (BOA) [1], Whale Optimization Algorithm (WOA) [32], Moth Flame Optimization (MFO) [31], Harris Hawks Optimization (HHO) [14], Monarch Butterfly Optimization (MBO) [53], Moth Search (MS) [51], Elephant Herding Optimization (EHO) [52], LSHADE-cnEpSin (later denoted as A1) [3], LSHADE-SPACMA (later denoted as A2) [35] and EBOwithCMAR (later denoted as A3) [22]. It is worth pointing out that last three algorithms performed the best at the recent competition based on CEC 2017 benchmark for global optimization [37]. The choice of algorithms included in the computational study is mainly driven by the availability of code, whether the code was accessible online, or was it provided to us upon request by the courtesy of authors.…”

Section: Comparison Based On Experimental Resultsmentioning

confidence: 99%

Wingsuit Flying Search—A Novel Global Optimization Algorithm

Covic

Lačević

2020

IEEE Access

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In this paper, a novel global optimization algorithm-Wingsuit Flying Search (WFS) is introduced. It is inspired by the popular extreme sport-wingsuit flying. The algorithm mimics the intention of a flier to land at the lowest possible point of the Earth surface within their range, i.e., a global minimum of the search space. This is achieved by probing the search space at each iteration with a carefully picked population of points. Iterative update of the population corresponds to the flier progressively getting a sharper image of the surface, thus shifting the focus to lower regions. The algorithm is described in detail, including the mathematical background and the pseudocode. It is validated using a variety of classical and CEC 2020 benchmark functions under a number of search space dimensionalities. The validation includes the comparison of WFS to several nature-inspired popular metaheuristic algorithms, including the winners of CEC 2017 competition. The numerical results indicate that WFS algorithm provides considerable performance improvements (mean solution values, standard deviation of solution values, runtime and convergence rate) with respect to other methods. The main advantages of this algorithm are that it is practically parameter-free, apart from the population size and maximal number of iterations. Moreover, it is considerably ''lean'' and easy to implement.

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Section: Comparison Based On Experimental Resultsmentioning

confidence: 99%

Wingsuit Flying Search—A Novel Global Optimization Algorithm

Covic

Lačević

2020

IEEE Access

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“…Thus, these benchmarks not only allow researchers to compare directly their results, but also give a ranking of the best algorithms for the benchmark (ranking that can be completed by the proposals published in journals). This makes it very easy to identify the current state-of-the-art for any given benchmark, thereby becoming a clear reference algorithm for comparing optimization techniques proposed in the future [317].…”

Section: Benchmarks and Comparison Methodsologiesmentioning

confidence: 99%

Bio-inspired computation: Where we stand and what's next

Ser

Osaba

Molina

et al. 2019

Swarm and Evolutionary Computation

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496

106

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“…CEC2010 benchmark was also used to evaluate the performance of HACC-D. Neither of the two algorithms, HDMR and HACC-D, outperformed the existing best results from previous competitions [31].…”

Section: Related Workmentioning

confidence: 99%

“…A comprehensive review and analysis regarding state-ofthe-art evolutionary algorithms participating using the latest CEC benchmarks can be found in [31].…”

Section: Related Workmentioning

confidence: 99%

LSHADE-SPA memetic framework for solving large-scale optimization problems

Hadi

Mohamed

Jambi

2018

Complex Intell. Syst.

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During the last decade, large-scale global optimization has been one of the active research fields. Optimization algorithms are affected by the curse of dimensionality associated with this kind of complex problems. To solve this problem, a new memetic framework for solving large-scale global optimization problems is proposed in this paper. In the proposed framework, success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration, while a modified version of multiple trajectory search is used for local exploitation. The framework introduced in this paper is further enhanced by the concept of divide and conquer, where the dimensions are randomly divided into groups, and each group is solved separately. The proposed framework is evaluated using IEEE CEC2010 and the IEEE CEC2013 benchmarks designed for large-scale global optimization. The comparison results between our framework and other state-of-the-art algorithms indicate that our proposed framework is competitive in solving large-scale global optimization problems.

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An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions

Cited by 76 publications

References 142 publications

Wingsuit Flying Search—A Novel Global Optimization Algorithm

Wingsuit Flying Search—A Novel Global Optimization Algorithm

Bio-inspired computation: Where we stand and what's next

LSHADE-SPA memetic framework for solving large-scale optimization problems

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