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Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion Optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved; the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, more economical and reliable distribution network. [ABSTRACT FROM AUTHOR]

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the problems and shortcomings of genetic algorithm, a hybrid genetic algorithm based on chaos genetic algorithm is designed in this paper. Based on the actual situation of universities, a mathematical model of timetabling problem is proposed. In view of the deficiency of genetic algorithm, chaos is introduced into the genetic algorithm by using the inherent regularity of chaotic sequence, effectively guiding crossover and mutation operation, and avoiding the defect that standard genetic algorithm is easy to fall into local minimum. The simulation of the course scheduling problem under the same conditions is conducted at the end of the paper, with the standard genetic algorithm and hybrid genetic algorithm. By comparing the calculation results, the results proved that the hybrid genetic algorithm is fully applicable to the scheduling problem, and has a high efficiency. At last, it can be concluded that the chaos genetic algorithm provides new ideas for timetabling problem. [ABSTRACT FROM AUTHOR]

In order to overcome the drawbacks of low computational accuracy and slow convergence rate of conventional artificial bee colony (ABC) algorithm during solving the function optimization problems, this paper proposed a self-adaptive ABC algorithm with enhanced search ability. The modified algorithm used the logic self-mapping function to generate chaotic sequences so as to implement chaos optimization for the search behaviors of employed bees, and it adopted adaptive step strategy of firefly algorithm to dynamically adjust the search behaviors of observer bees, the approach improved the local search ability. Simulations based on standard testing functions indicate that the modified ABC algorithm exhibits obviously improved optimization accuracy and convergence rate. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a hybrid methodology that exploits strengths of wavelet analysis and support vector machine model in forecasting time series, and deals with the application of proposed methodology in manufacturing time series forecasting. This method is characteristic of the preprocessing of sample data using wavelet transformation for forecast, i.e., the data sequence of evolvement of share of some sectors in manufacturing is first mapped into several time-frequency domains, and then a support vector machine is established for each domain. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective support vector machine models corresponding to different time-frequency domains. Nevertheless, one of disadvantages of the method is dilemma of selection of values of parameters in support vector machine because the way of selecting values for the parameters will affect the generalization performance remarkably. In this paper, chaos optimization is applied to accomplish selection of values of parameters. Results of experiments based on gross values of textile product in Japan suggest that this hybrid method can both achieve higher accuracy in manufacturing forecasting. [Copyright &y& Elsevier]

Fitting algorithms play an important role in the whole measuring cycle in order to derive a measurement result. They involve associating substitute geometry to a point cloud obtained by an instrument. This situation is more difficult in the case of non-linear geometry fitting since iterative method should be used. This article addresses this problem. Three geometries are selected as relevant case studies: circle, sphere and cylinder. This selection is based on their frequent use in real applications; for example, cylinder is a relevant geometry of an assembly feature such as pin-hole relationship, and spherical geometry is often found as reference geometry in high precision artifacts and mechanisms. In this article, the use of Chaos optimization (CO) to improve the initial solution to feed the iterative Levenberg–Marquardt (LM) algorithm to fit non-linear geometries is considered. A previous paper has shown the performance of this combination in improving the fitting of both complete and incomplete geometries. This article focuses on the comparison of the efficiency of different one-dimensional maps of CO. This study shows that, in general, logistic-map function provides the best solution compared to other types of one-dimensional functions. Finally, case studies on hemispheres and industrial cylinders fitting are presented. [ABSTRACT FROM AUTHOR]

Chaos has three important dynamic properties : the sensitive dependence on initial conditions, the intrinsic stochastic property and ergodicity. So it was applied to optimization problems. In most optimization methods such as the COA [1] and [4], variables chaos generated by different types of applications such as the Logistic, the application Tent, Lozi, Ikeda and others have shown very interesting results that random application. The main idea is to improve the convergence of the optimization chaotic. In this paper, a chaotic strategy is proposed based on a Hénon application chaotic. [ABSTRACT FROM AUTHOR]

SUMMARY Reactive power optimization plays an important role in safe and economic operation of power systems. However, with multiple mixed variables, this issue is well known as complex, nonlinear and multi-constrained. In this paper, combined particle swarm optimization (PSO) algorithm with chaos and multi-agent system, a novel algorithm (MACPSO) is developed and applied to reactive power optimization of power system. It synthesizes advantages of the swarm search of PSO and the intelligent search of agents. Seen as a particle of PSO during the search process, each agent competes and cooperates with the neighboring agents so as to obtain solutions of high quality swiftly. To strike a balance between intensification and diversification strategy and to reduce the probability of falling into local optimum, chaos optimization algorithm is adopted in the proposed algorithm. In order to verify the effectiveness of the proposed algorithm, the performance of MACPSO is evaluated on four classical testing functions and reactive power optimization. Experiment results show that MACPSO has excellent search ability and highly accurate convergence. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to propose and compare different one-dimensional maps as chaotic search patterns in the constraint nonlinear optimization problems. For this purpose, about 10 one-dimensional maps are introduced that can be used as search pattern in chaos optimization algorithms. We apply these maps in specific optimization algorithm (weighted gradient direction based chaos optimization algorithm) and compare them based on numerical simulation results. [Copyright &y& Elsevier]

The basic particle swarm (PSO)algorithm could not solve the traveling salesman problem (TSP) effectively. This paper analyzed the basic particle swarm optimization mechanism, and defined the new evolution equations in particle swarm optimization based evolutionary operators on the used of chaotic motion characteristics of randomness and ergodicity. It put forward a kind of improved chaotic particle with chaos optimization algorithm and particle swarm optimization algorithm. The algorithm was to adjust the inertia weight, introduced the chaotic carrier adjustment of the search strategy to avoid falling into local optimum, formed both global and local search hybrid discrete particle swarm algorithm, made it suitable for solving TSP combinatorial optimization problems. The algorithm was simulated by MATLAB. The results show that the precision of the algorithm, the convergence speed and optimization efficiency is better, also show that this algorithm is effectively in TSP application. It provides a reference method for solving the TSP problem. [ABSTRACT FROM AUTHOR]

Xiong, Q.; Zhang, H., and Rong, Q., 2020. Path planning based on improved particle swarm optimization for AUVs. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 279–282. Coconut Creek (Florida), ISSN 0749-0208. Path planning plays an important role in the motion of autonomous underwater vehicles (AUVs). It has always been a hot research topic. Aiming at the problems of traditional particle swarm optimization (PSO) in path planning, an improved PSO algorithm is proposed in this paper. In the initialization stage, the particles are initialized by a chaotic sequence. During algorithm processing, the sine function is used to dynamically decrease the particle weight. In the region near the optimal solution selected in each iteration of the PSO algorithm, a chaos algorithm is used to avoid falling into the local optimal value. Simulation experiment results show that the improved PSO algorithm greatly increases the effectiveness of the initial particles and can get the optimal path more quickly and accurately. [ABSTRACT FROM AUTHOR]

During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved. [ABSTRACT FROM AUTHOR]