Introductory Review of Swarm Intelligence Techniques

Thounaojam, Chinglemba,; Biswas, Soujanyo; Malakar, Debashish; Meena, V. L.; Sarkar, Debojyoti; Biswas, Anupam

doi:10.1007/978-3-031-09835-2_2

Cited by 5 publications

(2 citation statements)

References 73 publications

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“…Recent developments in SI have focused on enhancing the efficiency and accuracy of these algorithms, with applications ranging from parameter estimation in nonlinear systems to optimization in big data environments. The adaptability and scalability of SI-based algorithms make them suitable for a wide range of systems engineering challenges, from logistics and supply chain management to network design and energy systems optimization (Chinglemba, Biswas, Malakar, Meena, Sarkar, & Biswas, 2022).…”

Section: Applications and Implicationsmentioning

confidence: 99%

Optimization in Systems Engineering: A Review of How Data Analytics and Optimization Algorithms Are Applied

Oladele Junior Adeyeye,

Ibrahim Akanbi

2024

Comput. sci. IT res. j.

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This research review article provides a comprehensive examination of optimization techniques in systems engineering, highlighting their pivotal role in enhancing system performance, efficiency, and problem-solving capabilities. Through a structured exploration encompassing theoretical frameworks, methodologies, applications, and significant findings, the article synthesizes current knowledge and advancements in the field. It delves into various optimization methods, including traditional linear and nonlinear programming, alongside emerging trends such as swarm intelligence, nature-inspired algorithms, and the integration of machine learning. Case studies and recent research findings underscore the practical implications and effectiveness of these techniques across diverse engineering challenges. The review identifies key insights, demonstrating the versatility and potential of optimization techniques to drive innovation in systems engineering. Furthermore, it offers recommendations for future research directions and practical applications, emphasizing the importance of interdisciplinary approaches, algorithm development, and the adoption of advanced techniques in industry practices. This article aims to inform researchers and practitioners alike, fostering the continued evolution and application of optimization techniques in systems engineering. Keywords: Optimization Techniques, Systems Engineering, Swarm Intelligence, Machine Learning, Algorithm Development.

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Section: Applications and Implicationsmentioning

confidence: 99%

Optimization in Systems Engineering: A Review of How Data Analytics and Optimization Algorithms Are Applied

Oladele Junior Adeyeye,

Ibrahim Akanbi

2024

Comput. sci. IT res. j.

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“…Compared to experimentation or simulation, optimization algorithms are preferred due to their broader problem solving capabilities, reducing the need for human intervention. In recent years, integrating natural phenomena into algorithm design has significantly enhanced the efficiency of optimizing complex, multidimensional, non-continuous, non-differentiable, and noisy problem search spaces [15][16][17]. The function of a PID controller is to determine the system's steady-state error based on the actual setpoint and output values.…”

Section: Introductionmentioning

confidence: 99%

Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning

Chen,

Xiao,

Wang

et al. 2023

Applied Sciences

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In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.

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