A directional crossover (DX) operator for real parameter optimization using genetic algorithm

Das, Amit Kumar; Pratihar, Dilip Kumar

doi:10.1007/s10489-018-1364-2

Cited by 29 publications

(7 citation statements)

References 57 publications

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“…In order to evaluate the performance of iEFO, 13 commonly used benchmark functions [1, 5, 6, 13, 47, 48, 61, 66–72] with various dimensions, which are listed in Table 3, are used. In Table 3, D refers to the dimension of the problem.…”

Section: Computational Resultsmentioning

confidence: 99%

An Improved Electromagnetic Field Optimization for the Global Optimization Problems

Yurtkuran

2019

Computational Intelligence and Neuroscience

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Electromagnetic field optimization (EFO) is a relatively new physics-inspired population-based metaheuristic algorithm, which simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, the population consists of electromagnetic particles made of electromagnets corresponding to variables of an optimization problem and is divided into three fields: positive, negative, and neutral. In each iteration, a new electromagnetic particle is generated based on the attraction-repulsion forces among these electromagnetic fields, where the repulsion force helps particle to avoid the local optimal point, and the attraction force leads to find global optimal. This paper introduces an improved version of the EFO called improved electromagnetic field optimization (iEFO). Distinct from the EFO, the iEFO has two novel modifications: new solution generation function for the electromagnets and adaptive control of algorithmic parameters. In addition to these major improvements, the boundary control and randomization procedures for the newly generated electromagnets are modified. In the computational studies, the performance of the proposed iEFO is tested against original EFO, existing physics-inspired algorithms, and state-of-the-art meta-heuristic algorithms as artificial bee colony algorithm, particle swarm optimization, and differential evolution. Obtained results are verified with statistical testing, and results reveal that proposed iEFO outperforms the EFO and other considered competitor algorithms by providing better results.

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Section: Computational Resultsmentioning

confidence: 99%

An Improved Electromagnetic Field Optimization for the Global Optimization Problems

Yurtkuran

2019

Computational Intelligence and Neuroscience

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“…Then, the MFO variants and the M-MFO have been compared on dimension 30 to determine the top four superior MFO variants to participate in the next experiments. Following that, the performance of M-MFO has been compared with ten of the state-of-the-art swarm intelligence algorithms: KH [53], GWO [41], MFO [67], WOA [54], WCMFO [93], CMFO [94], HGSO [99], RGA-DX [50], ChOA [57], AOA [100], and ODSFMFO [98]. The parameters of the competitor algorithms are reported in Table 1.…”

Section: Numerical Experiments and Analysismentioning

confidence: 99%

“…Genetic algorithm (GA) [44], genetic programming (GP) [45], differential evolution (DE) [39], evolution strategy (ES) [46], and, from recent studies, quantum-based avian navigation optimizer algorithm (QANA) [47] are some evolutionary algorithms. During past years, many variants have been developed to improve the performance of evolutionary algorithms, such as enhanced genetic algorithm (EGA) [48], an ensemble of mutation strategies and control parameters with the DE (EPSDE) [49], the real-coded genetic algorithm using a directional crossover operator (RGA-DX) [50], and an effective multi-trial vector-based differential evolution (MTDE) [51].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the top four algorithms and eight other state-of-the-art swarm intelligence algorithms were considered for the main experiments. Hence, the total competitors for the main experiments were KH [53], GWO [41], MFO [67], WOA [54], WCMFO [93], CMFO [94], HGSO [99], RGA-DX [50], ChOA [57], AOA [100], and ODSFMFO [98]. The experimental evaluations and statistical tests revealed that the M-MFO algorithm outperforms other competitor algorithms with overall effectiveness of 91%.…”

Section: Introductionmentioning

confidence: 99%

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Migration-Based Moth-Flame Optimization Algorithm

et al. 2021

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Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

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“…Var olan algoritmaların iyileştirilmesinde araştırmacılar, değiştirilmiş ya da tamamen yeni operatörler ile mevcut algoritmanın performansını iyileştirmeye çalışmaktadırlar. Evrimsel operatörler [1], arama alanı azaltma tekniği [2] ve yeniden başlatma stratejileri [3] bu kategorinin örnekleri olarak sayılabilir. Farklı algoritmaların hibritleştirilmesi yönteminde, genel problemi çözmek adına farklı algoritmalar hibritleştirilmektedir [4].…”

Section: Introductionunclassified

Global Optimizasyon Problemleri için Kaotik Bonobo Algoritması

Bazna¹,

Akyol²

2021

European Journal of Science and Technology

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ÖzOptimizasyon algoritmaları, global optimumdan feragat edilerek yaklaşık en iyi çözümü bulmayı amaçlayan algoritmalardır. Bu çalışmada incelenen Bonobo Optimizasyon (BO) Algoritması ise sürü zekasına dayanan bir algoritma olup, bonoboların sosyal davranışlarının ve üreme stratejilerinin matematiksel modellenmesine dayanmaktadır. Bonobolar, yaşadıkları topluluk içinde çeşitli büyüklük ve özelliklerde gruplar oluşturarak, farklı amaçlar için birbirlerinden ayrılıp bir süre sonra yeniden bir araya gelmektedirler. Bonoboların üreme stratejisi incelendiğinde rasgele çiftleşme, kısıtlayıcı çiftleşme, konsorsiyum ve grup dışı çiftleşme gibi dört farklı stratejiyi benimsedikleri görülmektedir. Bonoboların bu doğal davranışları çeşitli optimizasyon problemlerini çözmek için kullanılmıştır. BO'yu diğer sürü zekasına dayalı algoritmalardan ayıran en önemli özelliği ise arama ajanlarının güncelleme mekanizmaları ve bunlarla ilişkili parametreler ve çiftleşme ortaklarının seçim yöntemidir. Bu çalışmada BO incelenip, algoritmada kullanılan parametreler Chebyshev, Circle, Gauss, Iterative, Logistic ve Tent kaotik haritaları kullanılarak yeniden üretilmiştir. Performansları karşılaştırmak için sekiz adet kalite testi fonksiyonu kullanılmıştır. Buna göre kaotik haritalar kullanılarak oluşturulan yeni algoritmalardan elde edilen sonuçların, klasik BO'ya göre daha verimli olduğu görülmüştür.

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A directional crossover (DX) operator for real parameter optimization using genetic algorithm

Cited by 29 publications

References 57 publications

An Improved Electromagnetic Field Optimization for the Global Optimization Problems

An Improved Electromagnetic Field Optimization for the Global Optimization Problems

Migration-Based Moth-Flame Optimization Algorithm

Global Optimizasyon Problemleri için Kaotik Bonobo Algoritması

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