Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy

Aziz, Mahdi; Tayarani-N., Mohammad-H.

doi:10.1016/j.swevo.2015.09.001

Cited by 8 publications

(3 citation statements)

References 53 publications

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“…The mathematical formulation of the benchmark functions are given in Table 1. In order to verify the performance of the proposed PTS algorithm, it is compared to other algorithms which have been reported in [5][6][7][8][9][10]. To carry out the comparison of algorithm performance, the approach using is to compare the accuracies for a fixed number of iterations.…”

Section: Results and Analysismentioning

confidence: 99%

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Projectile-target search algorithm: a stochastic metaheuristic optimization technique

Hiendro

2019

IJECE

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This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithms

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Section: Results and Analysismentioning

confidence: 99%

“…In order to examine the general performance of the proposed algorithm, it is tested on six benchmarks and their shifted functions. The performance is compared to other algorithms' results which have been reported in [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Projectile-target search algorithm: a stochastic metaheuristic optimization technique

Hiendro

2019

IJECE

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“…OBL is a fast growing research field in which a variety of new theoretical models and technical methods have been studied to deal with complex and significant problems [1], [40], [50], [54]. Recently, the idea of OBL has also been used to reinforce several global optimization methods such as differential evolution, particle swarm optimization, biogeography-based optimization, artificial neural network, bee and ant colony optimization [54], [5].…”

Section: A Opposition-based Learningmentioning

confidence: 99%

Opposition-Based Memetic Search for the Maximum Diversity Problem

Zhou

Hao

Duval

2017

IEEE Trans. Evol. Computat.

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As a usual model for a variety of practical applications, the maximum diversity problem (MDP) is computational challenging. In this paper, we present an opposition-based memetic algorithm (OBMA) for solving MDP, which integrates the concept of opposition-based learning (OBL) into the wellknown memetic search framework. OBMA explores both candidate solutions and their opposite solutions during its initialization and evolution processes. Combined with a powerful local optimization procedure and a rank-based quality-and-distance pool updating strategy, OBMA establishes a suitable balance between exploration and exploitation of its search process. Computational results on 80 popular MDP benchmark instances show that the proposed algorithm matches the best-known solutions for most of instances, and finds improved best solutions (new lower bounds) for 22 instances. We provide experimental evidences to highlight the beneficial effect of opposition-based learning for solving MDP.

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