A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications

Zhang, Quan; Du, Shiyu; Zhang, Yiming; Wu, Hongzhuo; Duan, Kai; Lin, Yanru

doi:10.3390/a15060189

Cited by 9 publications

(4 citation statements)

References 74 publications

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“…Another statistical test that ranks the performance of methods is Friedmans test, which contrasts at least three matched or paired methods. Each algorithm's fitness value is ranked by the Friedman test from low to high [ 58 ], to elucidate the statistical improvement and distinction achieved by GST-HBA, we employ a Friedmans test. Leveraging the data presented in Table 4 , Table 6 , the Friedmans Value (FV) obtained through Equation (19) is utilized, this value indicates significant improvement of an algorithm in relation to its compared counterparts.…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Adegboye,

Feda,

Ishaya

et al. 2023

Heliyon

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Adegboye,

Feda,

Ishaya

et al. 2023

Heliyon

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“…As an object's medium shifts the velocity shifts as well, bending in the direction of the boundary's normal. This theory aims to assist a candidate's solutions in leaving the sub-optimal while retaining variety 55 . This kind of opposition-based learning can be considered more advanced to avoid sub-optimality.…”

Section: Methodsmentioning

confidence: 99%

“…As a further validation of the performance of AEFA-CSR in real world optimization problem a well-known problem is chosen which is the welded beam design problem who was formulated by Rao 74 and used in CEC 2020 test function suite 75 . The welded beam design problem has several design parameters as outlined in 55 . There are four design variables that need to be determined x 1 , x 2 , x 3 and x 4 .…”

Section: Engineering Problems Application Optimization Of Antenna S-p...mentioning

confidence: 99%

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

Adegboye

Ülker

2023

Sci Rep

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Due to its low dependency on the control parameters and straightforward operations, the Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still has slow convergence and low solution precision. In this research, a hybrid Artificial Electric Field Employing Cuckoo Search Algorithm with Refraction Learning (AEFA-CSR) is suggested as a better version of the AEFA to address the aforementioned issues. The Cuckoo Search (CS) method is added to the algorithm to boost convergence and diversity which may improve global exploration. Refraction learning (RL) is utilized to enhance the lead agent which can help it to advance toward the global optimum and improve local exploitation potential with each iteration. Tests are run on 20 benchmark functions to gauge the proposed algorithm's efficiency. In order to compare it with the other well-studied metaheuristic algorithms, Wilcoxon rank-sum tests and Friedman tests with 5% significance level are used. In order to evaluate the algorithm’s efficiency and usability, some significant tests are carried out. As a result, the overall effectiveness of the algorithm with different dimensions and populations varied between 61.53 and 90.0% by overcoming all the compared algorithms. Regarding the promising results, a set of engineering problems are investigated for a further validation of our methodology. The results proved that AEFA-CSR is a solid optimizer with its satisfactory performance.

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“…The so-called group intelligence includes the behaviors of simple individuals and the whole group, exhibiting a specific intelligent feature without centralized control. Typical examples include ACO algorithm, PSO algorithm, artificial bee colony algorithm (ABC) [20], artificial fish swarming algorithm [21], grey wolf optimizer (GWO) [22], firefly algorithm (FA) [23], cuckoo search algorithm [24], chimp optimization algorithm [25], grasshopper optimization algorithm (GOA) [26], slime mold algorithm [27], and whale optimization algorithm (WOA) [28].…”

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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A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications

Cited by 9 publications

References 74 publications

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

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

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