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Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems

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IEEE ACCESS 2022年 10卷 121615-121640页

作者： Sadeeq, Haval Tariq Abdulazeez, Adnan Mohsin Duhok Polytech Univ Tech Coll Informat Akre Informat Technol Dept Duhok Iraq Duhok Polytech Univ Tech Coll Engn Duhok Energy Dept Duhok Iraq

Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization.

关键词： Giant trevally optimizer (GTO) optimization metaheuristics exploration exploitation benchmark functions constrained benchmark functions

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Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

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COMPUTER-AIDED DESIGN 2011年第3期43卷 303-315页

作者： Rao, R. V. Savsani, V. J. Vakharia, D. P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： Teaching-learning-based optimization constrained benchmark functions Mechanical design optimization

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Engineering optimization based on ideal gas molecular movement algorithm

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ENGINEERING WITH COMPUTERS 2017年第1期33卷 71-93页

作者： Varaee, Hesam Ghasemi, Mohammad Reza Univ Sistan & Baluchestan Dept Civil Engn Zahedan Iran

The present work introduces a new metaheuristic optimization method based on the ideal gas molecular movement (IGMM) to solve mathematical and engineering optimization problems. Ideal gas molecules scatter throughout the confined environment quickly. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized to accomplish the optimal solutions. To verify the performance of the IGMM algorithm, some mathematical and engineering benchmark optimization problems, commonly used in the literature, are inspected. Comparison of results obtained by IGMM with other optimization algorithms show that the proposed method has a challenging capacity in finding the optimal solutions and exhibits significance both in terms of the accuracy and reduction on the number of function evaluations vital in reaching the global optimum.

关键词： Optimization Metaheuristic algorithm Ideal gas molecular movement Engineering optimization problems constrained benchmark functions

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Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

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IEEE ACCESS 2022年 10卷 16150-16177页

作者： Oyelade, Olaide Nathaniel Ezugwu, Absalom El-Shamir Mohamed, Tehnan I. A. Abualigah, Laith Univ KwaZulu Natal Sch Math Stat & Comp Sci Pietermaritzburg Campus ZA-3201 Kwa Zulu South Africa Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classi

关键词： Optimization Viruses (medical) Diseases Metaheuristics Mathematical models Heuristic algorithms Statistics Ebola virus metaheuristic algorithm optimization problems constrained benchmark functions image classification convolutional neural network

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Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

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ENGINEERING OPTIMIZATION 2012年第12期44卷 1447-1462页

作者： Rao, R. V. Savsani, V. J. Balic, J. SV Natl Inst Technol Dept Mech Engn Surat Gujarat India Univ Maribor Fac Mech Engn Maribor Slovenia

An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, epsilon-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.

关键词： real-parameter optimization unconstrained benchmark functions constrained benchmark functions teaching-learning-based optimization

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

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ENGINEERING COMPUTATIONS 2021年第4期38卷 1554-1606页

作者： Kaveh, Ali Akbari, Hossein Hosseini, Seyed Milad Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Purpose This paper aims to present a new physically inspired meta-heuristic algorithm, which is called Plasma Generation Optimization (PGO). To evaluate the performance and capability of the proposed method in comparison to other optimization methods, two sets of test problems consisting of 13 constrained benchmark functions and 6 benchmark trusses are investigated numerically. The results indicate that the performance of the proposed method is competitive with other considered state-of-the-art optimization methods. Design/methodology/approach In this paper, a new physically-based metaheuristic algorithm called plasma generation optimization (PGO) algorithm is developed for solving constrained optimization problems. PGO is a population-based optimizer inspired by the process of plasma generation. In the proposed algorithm, each agent is considered as an electron. Movement of electrons and changing their energy levels are based on simulating excitation, de-excitation and ionization processes occurring through the plasma generation. In the proposed PGO, the global optimum is obtained when plasma is generated with the highest degree of ionization. Findings A new physically-based metaheuristic algorithm called the PGO algorithm is developed that is inspired from the process of plasma generation. Originality/value The results indicate that the performance of the proposed method is competitive with other state-of-the-art methods.

关键词： Metaheuristic algorithm Engineering problems Plasma generation optimization Truss structures constrained benchmark functions

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Master-Slave TLBO Algorithm for constrained Global Optimization Problems

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2021年第30期8卷 1-14页

作者： Mane, Sandeep U. Adamuthe, Amol C. Omane, Rajshree R. Shivaji Univ Kolhapur Rajarambapu Inst Technol Dept CSE Rajaramnagar MH India Shivaji Univ Kolhapur Rajarambapu Inst Technol Dept CS&IT Rajaramnagar MH India Amdocs Pune Maharashtra India

INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system's computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.

关键词： Master-slave TLBO algorithm Parallel Evolutionary Algorithms GPGPU constrained benchmark functions Optimization problems

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