Solving Ordinary Differential Equations With Adaptive Differential Evolution

Zhang, Zijia; Cai, Yaoming; Zhang, Dongfang

doi:10.1109/access.2020.3008823

Cited by 14 publications

(7 citation statements)

References 45 publications

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“…The assumptions are made to find the simplified differential equation form of reactor and given below: (Source: George Stephanopoulos 1990). [11][12][13] a) Correct mixing in the process reactor and jacket b) Steady value of volume reactor and jacket Mass balance equation is given as,…”

Section: Mathematical Modeling Of the Plantmentioning

confidence: 99%

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

Kaliappan¹,

Illango²,

Muthusamy³

et al. 2023

IASC

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This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional-Integral-Derivative (PID) controller for temperature control of Continuous Stirred Tank Reactor (CSTR) plant in chemical industries. The proposed work deals about the design of Differential Evolution (DE) algorithm in order to improve the performance of CSTR. In this, the process is controlled by controlling the temperature of the liquid through manipulation of the coolant flow rate with the help of modified Model Reference Adaptive Controller (MRAC). The transient response of temperature process is improved by using PID Controller, Differential Evolution Algorithm based PID and fuzzy based DE controller. Finally, the temperature response is compared with experimental results of CSTR.

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Section: Mathematical Modeling Of the Plantmentioning

confidence: 99%

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

Kaliappan¹,

Illango²,

Muthusamy³

et al. 2023

IASC

View full text Add to dashboard Cite

show abstract

“…Compared with the genetic algorithm, the differential evolution algorithm has a stagnation phenomenon, making the algorithm stop prematurely and fall into the optimal local solution prematurely. In the existing research, the strategy of adaptive improvement is effective [18]. By updating parameters adaptively through individual similarity and evolutionary state and designing evolutionary backtracking strategies to control population diversity, evolutionary algorithms can be well prevented from falling into premature results [19].…”

Section: A Evolutionary Algorithmsmentioning

confidence: 99%

Improved Adaptive Komodo Mlipir Algorithm

Liu

Zhang

2022

IEEE Access

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In order to improve the global search performance of the Komodo Mlipir Algorithm, this paper proposed two adaptive Komodo Mlipir Algorithms with variable fixed parameters (IKMA-1; IKMA-2). Among them, IKMA-1 adaptively controls the parthenogenesis radius of female Komodo dragons to achieve more efficient conversion of global search and local search. Second, IKMA-2 introduces adaptive weighting factors to the ''mlipir'' movement formula of Komodo dragons to improve the local search performance. Both IKMA-1 and IKMA-2 were tested on 23 benchmark functions in CEC2013 and compared with the other seven optimization algorithms. The Wilcoxon rank-sum test and Friedman rank test were used to compare the performance of different algorithms. Furthermore, IKMA-1 and IKMA-2 are applied to two constrained engineering optimization problems to verify the engineering applicability of the improved algorithm. The results show that both IKMA-1 and IKMA-2 have better convergence accuracy than the initial KMA. In terms of the benchmark function simulation results, IKMA-1 improves the performance by 17.58% compared to KMA; IKMA-2 improves by 10.99%. Both IKMA-1 and IKMA-2 achieve better results than other algorithms for engineering optimization problems, and IKMA-2 outperforms IKMA-1.INDEX TERMS Komodo Mlipir Algorithm, variable fixed parameters, adaptive optimization, engineering design optimization problems. I. INTRODUCTIONEstablishing models to deal with practical problems is an essential means of current academic research, and how to solve models faster and better depends on the actual solution performance of various algorithms. An optimization algorithm is an application technology based on mathematics and used to solve various practical optimization problems. At present, optimization algorithms can be mainly divided into data processing algorithms, neural network algorithms, and swarm intelligence algorithms [1]. Applied optimization problems widely exist in many fields, such as signal processing, production scheduling, medical applications, image processing, and path planning.However, since these optimization problems often involve discrete, discontinuous, and uncertain factors, it is not realistic to rely on a single algorithm to solve all optimization problems in life [2]. Therefore, the innovation of newThe associate editor coordinating the review of this manuscript and approving it for publication was Muhammad Zakarya .

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“…Differential evolution algorithm is a global optimization algorithm based on population adaptability [22]. Its advantages are high parallelism and randomness, and it has good global optimization ability.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Energy-Efficient Task Offloading Based on Differential Evolution in Edge Computing System With Energy Harvesting

Sun

Song

et al. 2021

IEEE Access

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To optimize the energy efficiency of edge computing system with energy harvesting, this paper proposes an energy-efficient task offloading method optimized by differential evolution. First, a wireless edge computing network model is established to analyze the energy harvesting, task offloading and task calculation of the system, as well as the total number of calculated bits and total energy consumption of the system. Second, according to the total number of calculated bits and total energy consumption of the system, an objective function is established to optimize the energy efficiency of system, and a differential evolution based optimization method is proposed, with which the optimal energy efficiency of system calculation, offloading time, calculation time and frequency are obtained. Experimental results show that the proposed method can not only achieve better convergence effect, but also can effectively solve the energy shortage problem of the micro-equipment and extend the service life of the equipment.INDEX TERMS Edge computing, task offloading, energy harvesting, differential evolutionary algorithm.PENG ZENG received the Ph.D. degree in mechatronic engineering from the Graduate School of the

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Solving Ordinary Differential Equations With Adaptive Differential Evolution

Cited by 14 publications

References 45 publications

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

Improved Adaptive Komodo Mlipir Algorithm

Energy-Efficient Task Offloading Based on Differential Evolution in Edge Computing System With Energy Harvesting

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