Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications

Gürses, Dildar; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza

doi:10.1515/mt-2020-0076

Cited by 47 publications

(3 citation statements)

References 76 publications

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“…Experimental results show that the algorithm has good exploration and exploitation ability. Gurses et al [ 110 ] applied a new hybrid slime mold algorithm, the Simulated Annealing Algorithm (HSMA-SA), to structural engineering design problems. Experimental results demonstrate the feasibility of the proposed algorithm in solving shape optimization problems.…”

Section: Discussionmentioning

confidence: 99%

Mutational Slime Mould Algorithm for Gene Selection

et al. 2022

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A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.

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Section: Discussionmentioning

confidence: 99%

Mutational Slime Mould Algorithm for Gene Selection

et al. 2022

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“…Using innovative or hybrid techniques, researchers tried to address their problems which many of the proposed methods can be used in other scientific or engineering domains. Self‐adaptive many‐objective based on decomposition in Champasak et al (2020), Multi‐surrogate‐assisted metaheuristics for crashworthiness optimisation in Aye et al (2019), Butterfly optimization algorithm for optimum vehicle designs in Yıldız, Yıldız, Albak, et al (2020), Hybrid Taguchi‐salp swarm optimization algorithm proposed in Yıldız and Erdaş (2021), hybrid grasshopper optimization algorithm in Yildiz et al (2021), arithmetic optimization algorithm, the slime mould optimization algorithm and the marine predators algorithm which are described in Gürses et al (2021), seagull optimization algorithm (SOA) which is proposed for optimizing the shape of a vehicle bracket in Panagant et al (2020) and the Henry gas solubility optimization (HGSO) algorithm in Yıldız, Yıldız, Pholdee, et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Plant competition optimization: A novel metaheuristic algorithm

Rahmani

AliAbdi

2022

Expert Systems

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Plant competition is a fundamental process in plant communities. In one neighbourhood, different plants compete with each other to access shared resources. This paper presents a novel evolutionary algorithm, plant competition optimization (PCO) algorithm, inspired by plant competition processes. In this algorithm, each feasible solution to an optimization problem is assumed to be a plant, with the underlying assumption that each plant grows in competition with its neighbours. In contrast to other techniques inspired by natural phenomena, we attempted to fit the formulation with a known plant growth model, Richards' growth model, and simulate what happens in nature. As the number of plants in a specific area increases, the available resources are decreased, and competition occurs in smaller areas. So, to use this aspect of competition, we develop some mathematical formulation to simulate the decrease of neighbouring area for each plant related to its size to compete on the share resources with the other neighbouring plants. This competition will conduct a smart local search around the most fitted solutions in the optimization context. Furthermore, the reproduction is simulated by producing some seeds in their neighbouring area, which a few of them can migrate to far distances as well. Summing up together, the most powerful plants can grow more. Under competition pressure, their neighbouring area will decrease more than the others, producing more seeds in the next generation. Just as happened in nature, the losers of this competition will die. Our algorithm's efficiency is shown by performing numerical tests on well‐known optimization problems and comparing the results with the other evolutionary algorithms. The results confirm that PCO is effective and efficient in finding sub‐optimal solutions and gives better results than genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), grasshopper optimisation algorithm (GOA), dragonfly algorithm (DA), salp swarm Algorithm (SSA), and comparable results with whale optimization algorithm (WOA) and marine predators algorithm (MPA) on multimodal optimization functions because it efficiently explores the entire search space efficiently and intelligently.

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“…Also, many experimental results show that, AOA provides very promising results in solving real-world engineering design. So, this study applied the AOA algorithm for fine-tuning the proposed fuzzy-PID controller parameters due to its promising results in solving several real-world engineering design problems [37]. Also, the AOA recently gave a distinguished performance in medicine field (i.e., evaluate images of COVID-19) [38].…”

Section: Introductionmentioning

confidence: 99%

Frequency Stability of AC/DC Interconnected Power Systems with Wind Energy Using Arithmetic Optimization Algorithm-Based Fuzzy-PID Controller

et al. 2021

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This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA’s proficiency has been verified over several other powerful optimization techniques; differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies; different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques.

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Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications

Cited by 47 publications

References 76 publications

Mutational Slime Mould Algorithm for Gene Selection

Mutational Slime Mould Algorithm for Gene Selection

Plant competition optimization: A novel metaheuristic algorithm

Frequency Stability of AC/DC Interconnected Power Systems with Wind Energy Using Arithmetic Optimization Algorithm-Based Fuzzy-PID Controller

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