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To improve safety, parking efficiency, smoothness and riding comfort of parallel parking of driverless vehicles at roadside, the characteristics of parallel parking conditions were analyzed, and the path planning was carried out by quintic polynomial curve. The mathematical model of path planning curve parameters optimization design was established with the abscissa of parking starting position as design variable, and six equality constraints were established based on parking boundary conditions. Three inequality constraints were established based on the requirements of curvature, collision avoidance and reducing parking space, and the weighted sum of the maximum curvature and the absolute value of abscissa of parking starting position was set as objective function. By the particle swarm optimization algorithm, the abscissa of the parking starting position was obtained, and six polynomial undetermined coefficients were calculated. The optimized smooth and continuous curvature planning path curve expression was obtained. Parking path tracking was realized based on the pure pursuit algorithm. The results show that the vehicle can track the optimized path precisely without collision with obstacles, which meets the safety requirements. The steering wheel angle changes continuously to meet the requirements of smoothness and ride comfort. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed energy resources integrated in distribution systems leads to an increasing need of continuously and automatically changing the system topology to realize the economic operation of distribution networks. This paper proposes an optimization model of dynamic reconfiguration for distribution networks based on a new method of time series analysis. Equivalent daily curve considering time‐varying nature of distributed generator and load demands is divided by an improved fuzzy C‐means clustering algorithm, where the indicator of section function is set to find the optimal reconfiguration time intervals. The uncertainty of distributed generator outputs and load demands is described by the interval algorithm. Then the affine Taylor expansion is adopted to solve the interval power flow equations. The reconfiguration optimization model is solved with decimal particle swarm optimization algorithm based on loop search. The optimal dynamic reconfiguration of a modified 70‐bus test system with distributed generators is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms. [ABSTRACT FROM AUTHOR]

This study constructs a reinforcement learning-based hybrid algorithm for Aquila Optimizer (AO) and improved Arithmetic Optimization Algorithm (IAOA). The point of the hybrid algorithm is that Q-learning can dynamically select the AO and the IAOA at different stages for different problems. In Arithmetic Optimization Algorithm (AOA), the mathematical optimization acceleration (MOA) function is restructured to balance global search and local exploitation, which can effectively stay away from the local optimum. Moreover, an improved reward function is modeled for Q-learning, which makes our hybrid algorithm more efficient and accurate. A set of benchmark functions and two engineering optimization problems are employed to test the performance of the proposed hybrid algorithm in this paper. Compared with other algorithms, the results show that the proposed hybrid algorithm has higher convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a new version of a hyper-heuristic framework: Generalized Self-Adapting Particle Swarm Optimization with samples archive (M-GAPSO). This framework is based on the authors previous works on hybridization of optimization algorithms and enhancing population based optimization with model based optimization. The paper presents the structure of the proposed framework and analyzes the impact of its modules on the final system performance. M-GAPSO hybridizes Particle Swarm Optimization, Differential Evolution and model based optimizers. A ratio of particular algorithms within a population is regulated by an adaptation scheme. The applicability of the proposed hybrid method to black-box optimization is verified on 24 continuous benchmark functions from the COCO test set and 29 functions from the CEC-2017 test set. On the BBOB test set a hybrid of PSO and DE with adaptation obtained 11 significantly better and 2 significantly worse results on 5 and 20 dimensional functions than the basic DE. Further inclusion of the model based optimizers led to 15 significantly better and 2 significantly worse results compared to the PSO-DE hybrid. On the CEC-2017 test set, M-GAPSO was significantly better than both Red Fox Optimization and Dual Opposition-Based Learning for Differential Evolution (DOBL) on 7 functions in 30 dimensions and 12 functions in 50 dimensions. • A hyper-heuristic approach to continuous global optimization problems. • An archive of function samples stored in an R-Tree based indexed. • Seamless hybridization of population-based and model-based optimizers. • A roulette-based algorithm selector. • Restart methods considering locations of discovered optima. [ABSTRACT FROM AUTHOR]

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems. • A BBO with improved migration and adaptive mutation (BBOIMAM) is proposed. • An improved migration strategy based on the sinusoidal and elite-learning is proposed. • An adaptive mutation strategy based on spring vibration is proposed. • BBOIMAM has the overall best performance on test functions and real applications. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods. [ABSTRACT FROM AUTHOR]

Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters' comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al's Test suite (ZDT), Deb et al's Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms. [ABSTRACT FROM AUTHOR]