Applications of new hybrid algorithm based on advanced cuckoo search and adaptive Gaussian quantum behaved particle swarm optimization in solving ordinary differential equations

Kumar, Nirmal; Shaikh, Ali Akbar; Mahato, Sanat Kumar; Bhunia, Asoke Kumar

doi:10.1016/j.eswa.2021.114646

Cited by 48 publications

(13 citation statements)

References 37 publications

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“…The PSO algorithm has good local search capabilities and strong convergence characteristics, 62–65 while the CS algorithm is highly random and has a strong ability to avoid the local optimal solution and global searchability. 66–68 In recent years, the PSO-CS algorithm has been applied in many fields, and many scholars have also verified that the PSO-CS coupling algorithm has many advantages. 69–74 By combining the PSO algorithm with the CS algorithm, each particle in the PSO algorithm is moving toward the current global optimum and the current local optimum.…”

Section: Methodsmentioning

confidence: 99%

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Cheng

Wang

Zeng

et al. 2022

Textile Research Journal

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Focusing on the “human–sport-clothing” system, this paper analyzed the influence of combinations of different tights and sport states on human body parts and overall body comfort from multiple dimensions. However, the motion state and some fabric parameters are non-numerical parameters, which could not be used for model analysis directly. In addition, there are too many numerical fabric parameters whose relationships are complicated, and it is difficult for general models to deal with these relationships, resulting in low accuracy of the comfort prediction model. Moreover, when using the artificial neural network to study comfort, it has some difficulties in expressing comfort and low prediction accuracy. To solve these problems, One-Hot was used to encode non-numerical parameters, and then intelligent algorithms were adopted to deal with these complex fabric parameters. Finally, a comfort prediction model was established in combination with an adaptive fuzzy reasoning system. The results showed that different fabric combinations and motion states had significant effects on local comfort (comfort of specific human body parts) and global comfort (whole body comfort). Moreover, the prediction model with non-numerical parameters has higher accuracy than the model without non-numerical parameters, which indicated that the prediction accuracy of the model had been improved after the introduction of One-Hot coding, so the non-numerical parameters cannot be ignored. The particle swarm optimization algorithm-cuckoo search algorithm-adaptive network-based fuzzy inference system hybrid model was superior to the particle swarm optimization algorithm-adaptive network-based fuzzy inference system and cuckoo search algorithm-adaptive network-based fuzzy inference system model in predicting local comfort and global comfort.

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Section: Methodsmentioning

confidence: 99%

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Cheng

Wang

Zeng

et al. 2022

Textile Research Journal

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“…Zanger et al explored the use of quantum computers to solve differential equations by using the basis encoding and fixedpoint arithmetic approach [9]. Moreover, Kumar et al [10] combined advanced cuckoo search (CS) algorithm along with adaptive Gaussian quantum behaved particle swarm optimization (AGQPSO) as a hybrid algorithm for solving second order differential equations. Converting these equations into unconstrained/bound constrained optimization problems is the main idea of the proposed algorithm.…”

Section: Related Workmentioning

confidence: 99%

State-Domain Equations and Their Quantum Computing Solution Based HHL Algorithm

Boudjoghra¹,

Daimellah²,

Zioui³

et al. 2022

MMEP

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Quantum computing is becoming a major new field in engineering. Adaptation of classical computation concepts to this emerging technological reality is underway. In this study, a sampling-based approach is used to adapt state-domain equations to HHL-based formulation to obtain a homologous quantum algorithm for solving the dynamic equations in the state domain. The results obtained by the quantum computing approach are very close to the real response of the system and to the power-series-based exact solution. This work represents a significant contribution to the advancement of dynamic systems modeling and solving in state-domain adapted for use in quantum computers in the future.

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“…In recent years, many optimization algorithms combining quantum theory and swarm intelligence algorithms have been proposed, such as quantum genetic algorithm (QGA), 26 quantum PSO algorithm, 27 quantum cuckoo search algorithm, 28 and hybrid algorithms based on quantum PSO algorithm. [29][30][31][32] QGA uses quantum bits to encode individuals, quantum superposition states and quantum collapse to express more information, and quantum rotation gates to update individuals, which gives it a completely different search mechanism compared with the GA. Compared with GA, QGA has the advantages of good population diversity, strong global search capability, and fast convergence speed, which makes it has obvious advantages in solving combinatorial optimization problems.…”

Section: Multilevel Adaptive Quantum Genetic Algorithmmentioning

confidence: 99%

Delay optimization for ternary fixed polarity Reed–Muller circuits based on multilevel adaptive quantum genetic algorithm

Xiaoqian

Wang

et al. 2021

Int J Intell Syst

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Delay optimization has now emerged as an important optimization goal in logic synthesis. The delay optimization for ternary fixed polarity Reed-Muller (FPRM) circuits aims to find a ternary FPRM circuit with a minimum delay. Because the delay optimization for ternary FPRM circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive quantum genetic algorithm (MAQGA), which divides individuals into three-level populations: high-level population, intermediate-level population, and low-level population and uses the proposed ternary quantum rotation gate, proposed ternary quantum correction gate, and proposed multi-operator adaptive mutation mechanism to make the three-level populations evolve. Moreover, based on the proposed delay decomposition strategy, we propose a delay optimization approach (DOA) for ternary FPRM circuits under the unit delay model, which searches for a ternary FPRM circuit with a minimum delay using the MAQGA. Experimental results demonstrated the effectiveness and superiority of the DOA in optimizing the delay of ternary FPRM circuits.

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Applications of new hybrid algorithm based on advanced cuckoo search and adaptive Gaussian quantum behaved particle swarm optimization in solving ordinary differential equations

Cited by 48 publications

References 37 publications

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

State-Domain Equations and Their Quantum Computing Solution Based HHL Algorithm

Delay optimization for ternary fixed polarity Reed–Muller circuits based on multilevel adaptive quantum genetic algorithm

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