Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems

Kaveh, A.; Akbari, Hossein; Hosseini, Seyed Milad

doi:10.1108/ec-05-2020-0235

Cited by 50 publications

(22 citation statements)

References 55 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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“…The plasma generation optimization (PGO) as a newly developed physics-based meta-heuristic algorithm is applied [34]. PGO is a population-based optimizer inspired by the process of plasma generation.…”

Section: Plasma Generation Optimization (Pgo) Algorithmmentioning

confidence: 99%

“…These processes occur iteratively through plasma generation. The main steps of the PGO algorithm can be stated as follows [34]:…”

Section: Plasma Generation Optimization (Pgo) Algorithmmentioning

confidence: 99%

A Comparative Study of the Optimum Tuned Mass Damper for High-rise Structures Considering Soil-structure Interaction

Kaveh

Ardebili

2021

Period. Polytech. Civil Eng.

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The present paper focuses on the optimum design of tuned mass damper (TMD) as a device for control of the structures. The optimum free vibration parameters such as period and damping ratio depend on the soil condition. For this reason, the seven meta-heuristic algorithms namely colliding bodies optimization (CBO), enhanced colliding bodies optimization (ECBO), water strider algorithm (WSA), dynamic water strider algorithm (DWSA), ray optimization (RO) algorithm, teaching-learning-based optimization (TLBO) algorithm and plasma generation optimization (PGO) are used to find the TMD parameters considering soil-structure interaction (SSI) effects. These optimization methods are applied to a benchmark 40-story structure. For comparison, the obtained results of these algorithms are compared. The capability and robustness of the algorithms are investigated through the benchmark problem. The results are shown that the soil type affects the optimum values of the TMD parameters, especially for the soft soil. To evaluate the performance of the obtained parameters in both the frequency and time domains, time history displacement and acceleration transfer function of the top story of the structure are calculated for the model with and without considering the SSI effects.

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“…A recently proposed plasma generation optimizer (PGO) [40] optimizer has shown unique characteristics, including (i) no need for parameter adjustments since the optimizer is completely parameter-free; (ii) excellent capabilities for exploration with plasma ionization generation; (iii) exploitation capabilities gained by de-excitation phase; (iv) inferior solutions can be eliminated during the excitation phase. With these four benefits, it is clear to see how the PGO algorithm excels.…”

Section: Introductionmentioning

confidence: 99%

MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

Kumar

Jangir²,

Tejani³

et al. 2021

IEEE Access

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A new multi-objective Plasma Generation Optimization (MOPGO) algorithm is suggested, and its non-dominated sorting (NDS) mechanism is investigated for numerous challenging real-world structural optimization design issues. The recently developed physics-based Plasma Generation Optimization (PGO) algorithm that works on excitation modes, deexcitation, and ionization plasma production systems is the inspiration behind this endeavor. As the search progresses, a better balance between exploration and exploitation has a more significant impact on the results; thus, the crowding distance feature is incorporated in the proposed MOPGO. Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristics. In truss design problems, minimization of the truss's mass and maximization of nodal displacement are considered as objective functions. In contrast, elemental stress and discrete cross-sectional areas are assumed to be behavior and side constraints, respectively. The usefulness of the MOPGO to solve complex problems is validated by eight truss-bar design problems. The efficacy of the MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposed MOPGO algorithm achieves the optimal solution with less computational complexity and has a better convergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted with multi-objective passing vehicle search, multi-objective slime mould algorithm, multi-objective symbiotic organisms search, and multi-objective ant lion optimization algorithms.

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Plasma generation optimization: a new physically-based metaheuristic algorithm for solving constrained optimization problems

Cited by 50 publications

References 55 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

A Comparative Study of the Optimum Tuned Mass Damper for High-rise Structures Considering Soil-structure Interaction

MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

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