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Optimization of constrained mechanical design problems using the equilibrium optimization algorithm

MATERIALS TESTING 2021年第6期63卷 552-559页

作者： Abderazek, Hammoudi Yildiz, Ali Riza Sait, Sadiq M. Mech Res Ctr CRM Constantine Algeria Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia

In this work, the optimization of structural and mechanical problems is carried out using the equilibrium optimizer (EO), which is a recent physical-based *** the ten-bar planar truss structure, planetary gearbox, hydrostatic thrust bearing, and robot gripper mechanism problems are solved using the EO algorithm. The results achieved using the EO in solving these problems are compared with those of recent algorithms. The computational results show that EO yields better outcomes and competitive results that can also be applied for the other problems studied.

关键词： Robot gripper mechanism planetary gearbox hydrostatic thrust bearing equilibrium optimizer algorithm physical-based algorithms

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Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 10733-10749页

作者： Shehadeh, Hisham A. Amman Arab Univ Fac Comp Sci & Informat Dept Comp Informat Syst Amman Jordan Amman Arab Univ Fac Comp Sci & Informat Dept Comp Sci Amman Jordan

This paper proposes a novel meta-heuristic optimization method, namely ''Chernobyl Disaster Optimizer (CDO)''. The underlying concepts and principles behind the proposed approach is inspired by the nuclear reactor core explosion of Chernobyl. In CDO, radioactivity happened because of nuclear instability, which different types of radiations are emitted from nuclei. The most common kinds of these radiations are called gamma, beta, and alpha particles. These particles fly away from the explosion point (high pressure point) to the low pressure point (the human standing point), which are harmful to the humans. The CDO mimics the process of nuclear radiation while attaching human after the nuclear explosion. The main steps of nuclear explosion and attaching human are implemented in which gamma, beta, and alpha particles are involved in this process. The CDO is evaluated with optimizing ''Congress on Evolutionary Computation (CEC 2017)'' test bed suites. In addition, it is compared against well-known optimization methods, such as ''Sperm Swarm Optimization'' and ''Gravitational Search Algorithm''. The experimental results prove its efficiency, which can be considered as viable alternative.

关键词： physical-based algorithms Swarm-based algorithms Sperm swarm optimization (SSO) Gravitational search algorithm (GSA) (CEC) 2017 benchmarks functions

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A hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA) for global optimization

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NEURAL COMPUTING & APPLICATIONS 2021年第18期33卷 11739-11752页

作者： Shehadeh, Hisham A. Amman Arab Univ Dept Comp Syst & Technol Fac Comp Sci & Informat Amman Jordan Amman Arab Univ Dept Comp Sci Fac Comp Sci & Informat Amman Jordan

This paper proposes a new hybrid optimization algorithm, called "(HSSOGSA)" with the combination of "gravitational search algorithm (GSA)" and "sperm swarm optimization (SSO)". The underlying concepts and ideas behind the proposed algorithm are to combine the capability of exploitation in SSO with the capability of exploration in GSA to synthesize both algorithms' strength. To evaluate the efficiency of the proposed approach, different test bed problems of optimization are considered, called the "congress on evolutionary computation (CEC)" 2017 suite. The proposed HSSOGSA is compared against both the standard GSA and SSO algorithms. These algorithms are compared based on two mechanisms, including, qualitative and quantitative tests. For the quantitative test, we adopt best fitness, standard deviation, and average measures, while for the qualitative test, we compare between the convergence rates achieved by the proposed algorithm and the convergence rates achieved by SSO and GSA. The outcomes of the study present the hybrid method possesses a better capability and performance to escape from local extremes with faster rate of convergence than the standard SSO and GSA for the majority of benchmarks functions of wide and narrow search space domain.

关键词： Swarm-based algorithms physical-based algorithms Sperm swarm optimization (SSO) Gravitational search algorithm (GSA) Hybrid algorithm (CEC) 2017 benchmarks functions

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