Chimp optimization algorithm

Khishe, Mohammad; Mosavi, Mohammad Reza

doi:10.1016/j.eswa.2020.113338

Cited by 785 publications

(392 citation statements)

References 36 publications

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“…Similarly, the number of output neurons is equivalent to the marine mammal classes, i. e., six neurons. For a comprehensive assessment of FWOA performance, this algorithm is compared with WOA 34 , ChOA 36 , PGO 37 , CVOA 38 , and BWO 39 benchmark algorithms. The basic parameters and the primary values of these benchmark algorithms get demonstrated in Table 2.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Classification of Marine Mammals Using Trained Multilayer Perceptron Neural Network With Whale Algorithm Developed With Fuzzy System

Saffari¹,

Khishe²

2020

Preprint

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The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activists field. In this paper, first, an experimental data set was created using a designed scenario. The Whale Optimization Algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference By setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, PGO, were also used for MLP-NN training. The measured criteria are concurrency speed, ability to avoid local optimization, and classification rate. The simulation results showed that FWOA has the highest classification accuracy in classifying both sets of marine mammal datasets and provides better results than the other five benchmark algorithms.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Classification of Marine Mammals Using Trained Multilayer Perceptron Neural Network With Whale Algorithm Developed With Fuzzy System

Saffari¹,

Khishe²

2020

Preprint

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“…Several artificial intelligence methods particularly the metaheuristic method have been used in solving ED problems in the past 5 years such as the chaotic bat algorithm (CBA) method which is a variant of the basic bat algorithm by entering chaotic sequences to improve its performance [4][5][6][7], Artificial Bee Colony Algorithm (ABC) which mimics the intelligent honeybee foraging behavior [8][9][10], grey wolf optimizer (GWO) inspired by hunting rules wolves and the grey wolf social hierarchy [11][12][13][14], and cuckoo search algorithm inspired by the interesting breeding behavior of cuckoo [15][16][17][18]. This paper will explore the potential of six metaheuristic methods, namely, Seagull Optimization Algorithm (SOA) [18], marine predator algorithm (MPA) [19], Sine Tree-Seed Algorithm (STSA) [20], Chimp Optimization Algorithm (ChOA) [21], Equilibrium Optimizer (EO) [22], and Giza Pyramids Construction (GPC) [23] in solving ELD problems. The test is using 2 different case studies and is based on several constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 3. The Basic Movement Of Chimpanzee Colonies a) Exploration phase and b) Exploitationphase[21] …”

mentioning

confidence: 99%

Comparison Study On Economic Load Dispatch Using Metaheuristic Algorithm

Aribowo

2022

Gazi University Journal of Science

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“…The performance of GBO on ELD is tested for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect and for various test networks. The performance of GBO is compared with compared with eight other metaheuristic algorithms such as Slime mould algorithm (SMA) [19], Elephant herding optimization (EHO) [20], Monarch butterfly optimization (MBO) [21], Moth search algorithm (MSA) [22], Earthworm optimization algorithm (EWA) [23], Artificial Bee Colony (ABC) Algorithm [24], Tunicate Swarm Algorithm (TSA) [25] and Chimp Optimization Algorithm (ChOA) [26].…”

Section: Introductionmentioning

confidence: 99%

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

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Chimp optimization algorithm

Cited by 785 publications

References 36 publications

Classification of Marine Mammals Using Trained Multilayer Perceptron Neural Network With Whale Algorithm Developed With Fuzzy System

Classification of Marine Mammals Using Trained Multilayer Perceptron Neural Network With Whale Algorithm Developed With Fuzzy System

Comparison Study On Economic Load Dispatch Using Metaheuristic Algorithm

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

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