An evaluation of the SNIFFER global optimization algorithm using standard test functions

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

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Two modifications of extension of piyavskii’s global optimization algorithm to a function continuous on a compact interval and its convergence

Herald of Tver State University Series Applied Mathematics ◽

10.26456/vtpmk624 ◽

2021 ◽

pp. 70-85

Author(s):

Владислав Иванович Заботин ◽

Павел Андреевич Чернышевский

Keyword(s):

Global Optimization ◽

Continuous Function ◽

Optimization Algorithm ◽

Lipschitz Continuity ◽

Compact Interval ◽

Compact Set ◽

Test Functions ◽

Global Optimization Algorithm ◽

Lipschitz Property ◽

Set Function

В работах R.J. Vanderbei доказано, что непрерывная на выпуклом компактном множестве функция обладает свойством $\varepsilon $-липшицевости, обобщающим классическое понятие липшицевости. На основе этого свойства R.J. Vanderbei предложено одно обобщение метода Пиявского поиска глобального минимума непрерывной на отрезке функции. В данной работе предлагаются одна модификация этого метода для положительной $\varepsilon $-константы и одна модификация для положительной $\varepsilon $-константы и условия останова, не зависящего от выбора $\varepsilon $. Доказана сходимость предлагаемых алгоритмов, приведены результаты численных экспериментов на основе применения разработанной программы. Данные методы могут быть применены для оптимизации любых непрерывных на отрезке функций, например, при решении некоторых обратных задачах баллистики и в экономике в прямых задачах потребительского выбора маршаллианского типа с переменными ценами благ и с непрерывной функцией полезности. R.J. Vanderbei in his works proves that any continuous on a compact set function has the $\varepsilon $-Lipschitz property which extends conventional Lipschitz continuity. Based on this feature Vanderbei proposed one extension of Piyavskii’s global optimization algorithm to the continuous function case. In this paper we propose one modification of the Vanderbei’s algorithm for a positive $\varepsilon $-constant and another modification for a positive $\varepsilon $-constant and $\varepsilon $ value independent termination condition. We prove proposed methods convergence and perform several computational experiments with designed software for known test functions.

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A Novel Particle Swarm Optimization Algorithm for Global Optimization

Computational Intelligence and Neuroscience ◽

10.1155/2016/9482073 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Chun-Feng Wang ◽

Kui Liu

Keyword(s):

Global Optimization ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Optimization Method ◽

Standard Test ◽

Test Functions ◽

Swarm Optimization ◽

Chaotic Search

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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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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