Atomic orbital search: A novel metaheuristic algorithm

Azizi, Mahdi

doi:10.1016/j.apm.2020.12.021

Cited by 199 publications

(66 citation statements)

References 34 publications

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“…The best solutions obtained by SNS and some of the other algorithms are presented in Table 12 . In addition, the performance of the PSO [ 71 ], DE [ 71 ], GA [ 71 ], hybrid particle swarm optimization (HPSO) [ 71 ], HPSO with Q-learning [ 71 ], CS [ 18 ], ISA [ 63 ], CGO [ 37 ], MGA [ 40 ], AOS [ 39 ], and SNS is summarized in Table 13 . The SNS algorithm obtains its results after 5000 evaluations, and its results are far better than those of other methods.…”

Section: Resultsmentioning

confidence: 99%

Social Network Search for Solving Engineering Optimization Problems

Bayzidi

Talatahari

Saraee

et al. 2021

Computational Intelligence and Neuroscience

151

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In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

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Section: Resultsmentioning

confidence: 99%

Social Network Search for Solving Engineering Optimization Problems

Bayzidi

Talatahari

Saraee

et al. 2021

Computational Intelligence and Neuroscience

151

View full text Add to dashboard Cite

show abstract

“…Some well-known methods of this category are Particle Swarm Optimization (PSO) [9], Ant Colony Optimization (ACO) [10], Artificial Bee Colony (ABC) [11], Cat Swarm Optimization (CSA) [12], Firefly Algorithm (FA) [13], and Krill Herd (KH) algorithm [14]. The third category consists of algorithms that are motivated by physical laws, such as Simulated Annealing (SA) [15], Harmony Search (HS) [16], Big-Bang Big-Crunch (BBBC) [17], Gravitational Search Algorithm (GSA) [18], Charged System Search (CSS) algorithm [19], Artificial Chemical Reaction Optimization Algorithm (ACROA) [20], Colliding Bodies Optimization (CBO) [21], Chaos Game Optimization (CGO) [22,23], and Atomic Orbital Search (AOS) [24] algorithm. Finally, metaheuristic approaches inspired by the lifestyle of animals or humans are classified in the fourth category, which includes Imperialistic Competitive Algorithm (ICA) [25], Cuckoo Search Algorithm (CSA) [26].…”

Section: -Presentmentioning

confidence: 99%

Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

2021

Self Cite

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A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics.

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“…In this paper, optimum design of engineering problems are considered by means of the Atomic Orbital Search (AOS) which is a recently proposed metaheuristic algorithm by Azizi [41]. This algorithm is developed based on the quantum-based atomic model and some basic and advanced principles of quantum mechanics governing the act of electrons around the nucleus of an atom.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm

2021

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In this paper, optimum design of engineering problems is considered by means of the Atomic Orbital Search (AOS), a recently proposed metaheuristic optimization algorithm. The algorithm is inspired by the quantum-based atomic model. In particular, the mathematical development of the algorithm is based on principles of quantum mechanics focusing on the act of electrons around the nucleus of an atom. For numerical investigation, 20 of well-known constrained design problems in different engineering fields are considered; some of which have been benchmarked by the 2020 Competitions on Evolutionary Computation (CEC 2020) for realworld optimization purposes. Statistical results including the best, mean, worst and standard deviation of multiple optimization runs are reported for the AOS algorithm. These results are compared to similar data from previous algorithms found in the literature to establish the efficiency and usefulness of the AOS algorithm. It is concluded that the AOS has acceptable behavior in dealing with all the considered constrained optimization problems while the maximum difference of about 40% between the best optimum values of the AOS and other approaches is noted for the robot gripper benchmark problem.

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Atomic orbital search: A novel metaheuristic algorithm

Cited by 199 publications

References 34 publications

Social Network Search for Solving Engineering Optimization Problems

Social Network Search for Solving Engineering Optimization Problems

Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

Optimization of Engineering Design Problems Using Atomic Orbital Search Algorithm

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