Performance of Repulsive Particle Swarm Method in Global Optimization of Some Important Test Functions: A Fortran Program

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

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Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201737 ◽

2021 ◽

pp. 1-12

Author(s):

Heming Jia ◽

Chunbo Lang

Keyword(s):

Global Optimization ◽

Heuristic Algorithm ◽

Experimental Results ◽

High Dimensional ◽

Lévy Flight ◽

Test Functions ◽

Levy Flight ◽

Salp Swarm Algorithm ◽

Swarm Algorithm ◽

Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

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A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽

10.3390/math9131477 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1477

Author(s):

Chun-Yao Lee ◽

Guang-Lin Zhuo

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Whale Optimization Algorithm ◽

Initial Population ◽

Test Functions ◽

Benchmark Test ◽

Thermal Exchange ◽

Whale Optimization ◽

Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

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Modified Flower Pollination Algorithm for Global Optimization

Mathematics ◽

10.3390/math9141661 ◽

2021 ◽

Vol 9 (14) ◽

pp. 1661

Author(s):

Mohamed Abdel-Basset ◽

Reda Mohamed ◽

Safaa Saber ◽

S. S. Askar ◽

Mohamed Abouhawwash

Keyword(s):

Global Optimization ◽

Nonlinear Equation ◽

Differential Evolution ◽

Flower Pollination Algorithm ◽

Test Cases ◽

Test Functions ◽

Flower Pollination ◽

New Variant ◽

Experimental Findings ◽

Nonlinear Equation Systems

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

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Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015500177 ◽

2015 ◽

Vol 24 (05) ◽

pp. 1550017 ◽

Cited By ~ 1

Author(s):

Aderemi Oluyinka Adewumi ◽

Akugbe Martins Arasomwan

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Search Space ◽

Particle Swarm Optimizer ◽

Search Range ◽

Original Algorithm ◽

Inertia Weight ◽

And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

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