Evolutionary mating algorithm

Sulaiman, Mohd Herwan; Mustaffa, Zuriani; Saari, Mohd Mawardi; Daniyal, Hamdan; Mirjalili, Seyedali

doi:10.1007/s00521-022-07761-w

Cited by 37 publications

(9 citation statements)

References 62 publications

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“…These algorithms are mainly based on the evolutionary process of organisms in the natural world and adopt the "survival of the fittest" theory to realize the optimization of the search space. Cooperative co-evolutionary algorithms (CCEA) 22 , evolutionary mating algorithms (EMA) 23 , evolutionary field optimization algorithms (EFO) 24 , and quantum-based avian navigation optimizer algorithm (QANA) 25 belong to this type of algorithm. The main search mechanisms of such algorithms are crossover and mutation.…”

Section: Introductionmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

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The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.

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Section: Introductionmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…They begin by creating a random population, which then evolves through selection, crossover, and mutation operations. The most popular evolutionary algorithms are Genetic Algorithm (GA) (developed in 1975) [2]; Differential Evolution (DE) (developed in 1997) [3]; Harmony Search (HS) (developed in 2001) [4]; Black Widow Optimization Algorithm (BWO) (developed in 2020) [5]; Learner Performance Based Behavior algorithm (LPB) (developed in 2021) [6]; Evolutionary Mating Algorithm, based on the adoption of the random mating concept from the Hardy-Weinberg equilibrium and crossover index [7]; and One-Dimensional Subspaces Optimization Algorithm (1D-SOA) [8]. Although evolutionary algorithms have seen successful application in some problems, these algorithms slowly converge to the optimal solutions and become stuck in local optima.…”

Section: Introductionmentioning

confidence: 99%

The Pine Cone Optimization Algorithm (PCOA)

Valikhan Anaraki,

Farzin

2024

Biomimetics

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The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. It employs new and powerful operators to simulate the mentioned mechanisms. The performance of PCOA is analyzed using classic benchmark functions, CEC017 and CEC2019 as mathematical problems and CEC2006 and CEC2011 as engineering design problems. In terms of accuracy, the results show the superiority of PCOA to well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO). The results of PCOA are competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, the results of PCOA are reasonable. According to the Friedman test, PCOA’s rank is 1.68 and 9.42 percent better than EBOwithCMAR (second-best algorithm) and LSHADE (third-best algorithm), respectively. The authors recommend PCOA for science, engineering, and industrial societies for solving complex optimization problems.

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“…It can be seen that the number of heuristic-based methods is larger than that of deterministic methods. In addition, there are many newly developed algorithms such as Henry gas solubility optimization (HGSO) [19], a coronavirus disease optimization algorithm [20], prairie dog optimization [21], evolutionary mating algorithm [22], etc. and they have also been successfully applied to many problems in electrical engineering such as power quality disturbance [23], optimal power flow [24][25][26] and distributed generator placement [27].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of photovoltaic module relied on golden jackal optimization

2024

Archives of Electrical Engineering

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Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.

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Evolutionary mating algorithm

Cited by 37 publications

References 62 publications

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

The Pine Cone Optimization Algorithm (PCOA)

Parameter estimation of photovoltaic module relied on golden jackal optimization

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