A Novel Metaheuristic Algorithm: The Team Competition and Cooperation Optimization Algorithm

Wu, Tao; Wang, Xinyu; Chen, Jingjue; Chen, Xi; Ashrafzadeh, Amir Homayoon

doi:10.32604/cmc.2022.028942

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…During the learning section, each student randomly learns from the other students. Examples of popular human-based algorithms are brain storm optimization [37], imperialist competitive algorithm [38], cultural algorithm [39], coronavirus herd immunity optimization [40], and team competition and cooperation optimization algorithm [41].…”

Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Li¹,

Du²,

Zhang³

et al. 2023

Computers, Materials &Amp; Continua

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Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA's capability, the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms, including artificial bee colony algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm, grasshopper optimization algorithm, and whale optimization algorithm. The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems. DFA is applied to five engineering projects to demonstrate its applicability. The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms. Finally, potential upgrading routes for DFA are proposed as possible future developments.

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Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Li¹,

Du²,

Zhang³

et al. 2023

Computers, Materials &Amp; Continua

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“…Sine Cosine Algorithm (SCA) [12], Circle-Inspired Optimization Algorithm (CIOA) [13] PhyA Simulated Annealing Algorithm (SA) [14], Gravitational Search Algorithm (GSA) [15] EvoA Genetic Algorithm (GA) [16], Differential Evolution (DE) [17] HuSoA Teaching-learning-based Optimization (TLBO) [18], Team Competition and Cooperation Optimization Algorithm (TCCO) [19]…”

Section: Maamentioning

confidence: 99%

“…EvoA algorithms are inspired by the search Algorithms of biological evolutionary mechanisms such as natural selection and genetics; some classical EvoA algorithms include GA [16] and DE [17]. HuSoA algorithms are generally derived from human social phenomena and activities; some classical HuSoA algorithms include, TLBO [18] and TCCO [19]. PlA algorithms are based on intelligent behavior in plants, including IWO [20] and PA [21].…”

Section: Plamentioning

confidence: 99%

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FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

Chen,

Zhao,

2023

Mathematics

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The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality.

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Power allocation method based on modified social network search algorithm

Gao,

Li,

Lin

et al. 2024

Appl Intell

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A Novel Metaheuristic Algorithm: The Team Competition and Cooperation Optimization Algorithm

Cited by 3 publications

References 20 publications

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

Power allocation method based on modified social network search algorithm

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