Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems

Pierezan, Juliano; Coelho, Leandro dos Santos

doi:10.1109/cec.2018.8477769

Cited by 372 publications

(227 citation statements)

References 28 publications

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“…Evolutionary algorithms (EAs) is invented not more than 28 years and is not pretty old computational algorithm [12]. Research studies have been accomplished new evolutionary algorithms in engineering and computational science [13,14,15]. EAs are known as population based algorithm.…”

Section: Nature-inspired Evolutionary Computationmentioning

confidence: 99%

“…Thus, COA seems to be one particular type of Grey Wolf Optimizer (GWO) as COA just does the third step of GWO. COA is presented recently in [13] by Pierezan and Coelho in 2018 to solve large-scale optimization problems. Algorithm 6 presents a general overview of COA for feature selection.…”

mentioning

confidence: 99%

“…Algorithm 6 Implementation of COA algorithm for feature selection [13] Input: S = {x 0 , x 1 , x 2 , ..., x n } which consists o f N p ∈ N * andN c ∈ N * are initialized usin soc p,t c,j = lower Boundj + j · (upper Boundj − lower Boundj ), t=0 , max iteration ≥ 0, Best ca otes = ∅.…”

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confidence: 99%

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Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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purpose. This motivates researchers to think about optimization and apply nature inspired algorithms, such as meta-heuristic and evolutionary algorithms (EAs) to solve large-scale optimization problems. Building on the strategies of these algorithms, researchers solve large-scale engineering and computational problems with innovative solutions. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. To that end, researchers try to run their algorithms more than usually suggested, around 20 or 30 times, then they compute the mean of result and report only the average of 20 / 30 runs' result. This high number of runs becomes necessary because EAs, based on their randomness initialization, converge the best result, which would not be correct if only relying on one specific run. Certainly, researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.A large number of engineering, science and computational problems have yet to be solved in a more computationally efficient way. One of the emerging challenges is the evolving technologies and how they enhance towards autonomy. This leads to collection of large amount of data from various sensing and measurement technologies, such as cameras, smart phones, health sensors, and environment sensors. Hence, generation, manipulation and illustration of data grow significantly. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. Therefore, data plays a pivotal role in technologies by introducing several challenges: how to present, what to present, why to present. Researchers explored various approaches to implement a comprehensive solution to express their results in every particular domain, such that the solution enhances the performance and

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Section: Nature-inspired Evolutionary Computationmentioning

confidence: 99%

mentioning

confidence: 99%

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Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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“…The solution of such engineering problems makes optimization a desirable element in applied modeling. Optimization refers to the process of analyzing, modeling, and solving analytical or numerical problems to determine the solutions that achieve a quantitative goal while considering potential constraints . Many real‐life problems can be formulated as an optimization program that aims to minimize or maximize cost .…”

Section: Introductionmentioning

confidence: 99%

“…Optimization refers to the process of analyzing, modeling, and solving analytical or numerical problems to determine the solutions that achieve a quantitative goal while considering potential constraints. 1 Many real-life problems can be formulated as an optimization program that aims to minimize or maximize cost. 2 For a given problem, a mathematical model refers to a system that describes the structure and identification of significant variables using mathematical concepts.…”

Section: Introductionmentioning

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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An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

Parouha

Verma²

2020

Int Trans Electr Energ Syst

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Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems

Cited by 372 publications

References 28 publications

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

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

An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

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