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A Modified Oppositional chaotic local search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

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Journal of Bionic Engineering 2023年第4期20卷 1828-1851页

作者： Serdar Ekinci Davut Izci Laith Abualigah Raed Abu Zitar Department of Computer Engineering Batman University72100BatmanTurkey Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi MinhVietnam Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800PenangPulau PinangMalaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient *** this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective *** construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning *** also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters *** show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark ***2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO *** the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection *** also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider *** excellent performance of the proposed method is also illustrated through different quality indicators and different operating ***,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic *** show

关键词： Aquila optimizer chaotic local search Modified opposition-based learning Real PIDD^(2)controller Vehicle cruise control system Bionic engineering

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An Improved Artificial Rabbits Optimization Algorithm with chaotic local search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

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Computer Modeling in Engineering & Sciences 2024年第11期141卷 1067-1110页

作者： Feyza AltunbeyÖzbay ErdalÖzbay Farhad Soleimanian Gharehchopogh Department of Software Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia44867-57159Iran

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local *** overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)*** adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space *** maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive *** proposed COARO algorithm has been tested using thirty-three distinct benchmark *** outcomes have been compared with the most recent optimization ***,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other *** study also introduces a binary variant of the continuous COARO algorithm,named *** performance of BCOARO was evaluated on the breast cancer *** effectiveness of BCOARO has been compared with different feature selection *** proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness *** experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

关键词： Artificial rabbit optimization binary optimization breast cancer chaotic local search engineering design problem opposition-based learning

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Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

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Journal of Central South University 2013年第6期20卷 1572-1581页

作者：谭跃谭冠政邓曙光 School of Information Science and Engineering Central South University School of Communication and Electronic Engineering Hunan City University

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.

关键词： particle swarm optimization differential evolution chaotic local search reliability-redundancy allocation

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Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation Problem

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Computers, Materials & Continua 2022年第12期73卷 5245-5268页

作者： Nashwa Nageh Ahmed Elshamy Abdel Wahab Said Hassan Mostafa Sami Mustafa Abdul Salam Faculty of Science Suez Canal UniversityIsmailiaEgypt Faculty of Computers and Artificial Intelligence Helwan UniversityHelwanEgypt Faculty of Computers and Artificial Intelligence Benha UniversityBenha 13518Egypt Faculty of Computer Studies Arab Open UniversityCairoEgypt

Team Formation(TF)is considered one of the most significant problems in computer science and *** is defined as forming the best team of experts in a social network to complete a task with least *** real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF *** has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation *** paper proposes two improved swarm-based algorithms for solving team formation *** first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)*** also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima *** second algorithm is the chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search *** HBO’s optimization process,a logistic chaotic map is *** performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and ***,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.

关键词： Team formation problem optimization problem genetic algorithm heap-based optimizer simulated annealing hybridization method chaotic local search

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