Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.

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Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

Processes ◽

10.3390/pr9101806 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1806

Author(s):

Yuedong Zhang ◽

Yuanbin Mo

Keyword(s):

Dynamic Optimization ◽

Production Efficiency ◽

Chemical Engineering ◽

Optimization Problems ◽

Optimal Solution ◽

Population Diversity ◽

Equal Division ◽

Dynamic Optimization Problems ◽

Increase Production Efficiency ◽

Chaotic Mapping

The optimal solution of the chemical dynamic optimization problem is the basis of automatic control operation in the chemical process, which can reduce energy consumption, increase production efficiency, and maximize economic benefit. In this paper, a modified sailfish optimizer (MSFO) combined with an equal division method is proposed for solving chemical dynamic optimization problems. Based on the basic sailfish optimizer, firstly, the tent chaotic mapping strategy is introduced to disturb the initialization of sailfish and sardine populations to avoid the loss of population diversity. Secondly, an adaptive linear reduction strategy of attack parameters is proposed to enhance the exploration and exploitation ability of sailfish. Thirdly, the updating formula of sardine position is modified, and the global optimal solution is used to attract all sardine positions, which can avoid the premature phenomenon of the algorithm. Eventually, the MSFO is applied to solve six classical optimization cases of chemical engineering to evaluate its feasibility. The experimental results are analyzed and compared with other optimization methods to prove the superiority of the MSFO in solving chemical dynamic optimization problems.

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Genetic Algorithms with Immigrants Scheme for Dynamic Optimization Problems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1379 ◽

2013 ◽

Vol 333-335 ◽

pp. 1379-1383

Author(s):

Yan Wu ◽

Xiao Xiong Liu

Keyword(s):

Genetic Algorithms ◽

Dynamic Optimization ◽

Optimization Problems ◽

Main Idea ◽

Optimal Solution ◽

Search Space ◽

Dynamic Environments ◽

Dynamic Optimization Problems ◽

New Space ◽

Over Time

In dynamic environments, it is difficult to track a changing optimal solution over time. Over the years, many approaches have been proposed to solve the problem with genetic algorithms. In this paper a new space-based immigrant scheme for genetic algorithms is proposed to solve dynamic optimization problems. In this scheme, the search space is divided into two subspaces using the elite of the previous generation and the range of variables. Then the immigrants are generated from both the subspaces and inserted into current population. The main idea of the approach is to increase the diversity more evenly and dispersed. Finally an experimental study on dynamic sphere function was carried out to compare the performance of several genetic algorithms. The experimental results show that the proposed algorithm is effective for the function with moving optimum and can adapt the dynamic environments rapidly.

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The Use of a Random Algorithm for Dynamic Optimization of Mechanisms

Journal of Engineering for Industry ◽

10.1115/1.3439131 ◽

1977 ◽

Vol 99 (1) ◽

pp. 153-156 ◽

Cited By ~ 3

Author(s):

B. Z. Sandler

Keyword(s):

Spectral Theory ◽

Dynamic Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Random Processes ◽

Dynamic Optimization Problems ◽

Vibrating System ◽

Random Algorithm ◽

Number Combination

This paper describes the following two kinds of dynamic optimization problems applied to mechanisms and their solution by using the spectral theory of random processes: (1) One has to choose such parameters of three-mass vibrating system which will insure the minimum vibration of the most important element. (2) The possibility is envisioned of achieving output motion with minimal errors in a gearing mechanism, without increasing the accuracy of the wheels. It is suggested to achieve this effect by the choice of optimal teeth number combination. A randomized optimization algorithm is considered for these aims.

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