A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

Liu, Yu; Zhao, Yiran; Liu, Jingsen

doi:10.4018/ijdst.2021010104

Cited by 4 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this time, the three parameters k p , k i , and k d need to be set manually. Combining the neural network technology with the traditional PID control, it can solve the traditional PID controller to a certain extent, which makes it not easy for it to carry out online real-time parameter tuning and other defects, and give full play to the advantages of PID control [6]. e proportional-integralderivative (PID) control approach is the most often used and is well known in industrial control.…”

Section: Introductionmentioning

confidence: 99%

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

Zhang

Srividya

Kakaravada

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

To improve the speed of global optimization algorithm, a class of global optimization algorithms for intelligent electromechanical control system with improved filling function is proposed. By attaching the intelligent managing system improving algorithm and the filling function procedure, the algorithm can stand out from the current particular optimal solution, avoid the phenomenon of falling into the local favorable solution in the process of algorithm iteration, make the algorithm find a better solution, and improve the efficiency of solving the multiextremum global improving problem. Multiextremum-seeking is an optimal control technique that works with unknown conditions while assuming that measurements of the plant’s input and output signals are accessible. The presented work is for an electromechanical system which will handle the low accuracy and untimely tendency of conventional systems which are used in various practical applications. Few learning algorithms have been developed to explicitly optimize mean average precision (MAP) due to computational constraints. The outcomes show that the convergence of the test functions F6 and F7 is not good when the MAPID algorithm is only used for optimization. The MAPID_FF algorithm not only ensures the convergence and optimization precision of the two test functions, but also reduces the optimization time compared with the filling function method. Compared with the filling function method, the improved algorithm has higher accuracy and faster speed, and it is not simple to fall into the local optimum, so the global optimal value is more accurate.

show abstract

Section: Introductionmentioning

confidence: 99%

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

Zhang

Srividya

Kakaravada

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…( 2020c ) ISCA Distributed generators (DGs) allocation Superiority of ISCA compared to HSA, GA, FWA, RGA, and FF algorithms Raut and Mishra ( 2020a ) LSC-SSA + Training muti-layer perceptron neural network LSC-SSA gives better results in terms of classification accuracy in comparison with PSO, FA, SSA, WOA, and SCA Zhang and Wang ( 2020 ) LSCA – Global Optimization Problems LSCA provides good convergence accuracy Li et al. ( 2021c ) Fuzzy-Based SCA SCA-ANFIS – Oil Consumption Forecasting Effectiveness of SCA-ANFIS compared to the traditional ANFIS, GA-ANFIS, PSO-ANFIS, GWO-ANFIS, and WOA-ANFIS techniques Al-Qaness et al. ( 2018 ) SCA-NLSF – Optimum capacitor allocation in distribution systems Effectiveness of SCA-NLSF compared to DE and PSO algorithms in terms of power losses and energy cost Kamel et al.…”

Section: Recent Variants Of the Sine Cosine Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

View full text Add to dashboard Cite

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

show abstract

A Learning Sparrow Search Algorithm

Ouyang

Zhu

Wang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper solves the drawbacks of traditional intelligent optimization algorithms relying on 0 and has good results on CEC 2017 and benchmark functions, which effectively improve the problem of algorithms falling into local optimality. The sparrow search algorithm (SSA) has significant optimization performance, but still has the problem of large randomness and is easy to fall into the local optimum. For this reason, this paper proposes a learning sparrow search algorithm, which introduces the lens reverse learning strategy in the discoverer stage. The random reverse learning strategy increases the diversity of the population and makes the search method more flexible. In the follower stage, an improved sine and cosine guidance mechanism is introduced to make the search method of the discoverer more detailed. Finally, a differential-based local search is proposed. The strategy is used to update the optimal solution obtained each time to prevent the omission of high-quality solutions in the search process. LSSA is compared with CSSA, ISSA, SSA, BSO, GWO, and PSO in 12 benchmark functions to verify the feasibility of the algorithm. Furthermore, to further verify the effectiveness and practicability of the algorithm, LSSA is compared with MSSCS, CSsin, and FA-CL in CEC 2017 test function. The simulation results show that LSSA has good universality. Finally, the practicability of LSSA is verified by robot path planning, and LSSA has good stability and safety in path planning.

show abstract

A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

Cited by 4 publications

References 36 publications

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

A comprehensive survey of sine cosine algorithm: variants and applications

A Learning Sparrow Search Algorithm

Contact Info

Product

Resources

About