Adaptive Random-key Particle Swarm Optimization with DC-closure Local Search for a Two-stage Fixed-charge Transportation Benchmark

In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability.

The Particle Swarm Optimization (PSO) algorithm offers the advantages of fast convergence speed and a simple structure for digital filter design. However, it is prone to premature convergence, often settling in local optima. Improved PSO algorithms are being continuously developed. Starting from the sine adjustment of the acceleration coefficient in the PSO algorithm speed update formula, a Sine Function Particle Swarm Optimization (SFPSO) algorithm was implemented and used to design finite impulse response (FIR) low-pass digital filters. Simultaneously, it was compared with the standard PSO, Adaptive Particle Swarm Optimization (APSO), and Opposition-Based Particle Swarm Optimization (OPSO) algorithms. The research results show that the FIR low-pass digital filter designed by SFPSO algorithm performed the best in convergence speed and accuracy; the transition zone was the steepest; the passband ripple (0.4046 dB) was very close to the optimal value of the four algorithms (0.4021 dB), with the lowest passband standard deviation, therefore the passband performance was excellent; the stopband ripple was only lower than APSO, the stopband attenuation is only lower than OPSO, and the stopband high-frequency filtering performance is good.

Swarm Intelligence algorithms, in many optimization problems, have constantly served a purpose of global search method. One of the problems confronted during optimization is clustering problem. Input for a clustering process is a set of data which are then organized into a number of sub-groups. Modern studies have recommended that partitioned or segregated clustering algorithms are more appropriate for clustering of wide and huge datasets. One of the most frequent partitional clustering algorithms is K-Means. K-means algorithm shows a more rapid convergence than PSO but then against local optimal area is generally trapped depending on the random values of initial centroids. An efficient hybrid method is presented in this paper, namely particle swarm optimization with fuzzy logic or adaptive particle swarm optimization (APSO) to resolve data clustering problem. The PSO algorithm does find a good or near optimal solution in reasonable time, but its presentation was enhanced by seeding the initial swarm with fuzzifier function. The adaptive fuzzy particle swarm optimization algorithm (APSO) is compared with k-means using total execution time and clustering group error. It is discovered that the total execution time for APSO method outperforms the k-means and had higher solution quality in terms of clustering group error.

In this article we describe a novel Adaptive particle swarm optimization (APSO) algorithm based on population diversity information. It is presented to solve the precocious convergence problem of particle swarm optimization algorithm. The APSO algorithm uses the information of the population diversity to adjust nonlinearly inertia weight. Velocity mutation factor and position interchange factor are both introduced and the global performance is clearly improved. The APSO algorithm is applied to optimization of parameters in the optimal model based on support vector machine (SVM). SVM is a popular classification method with many diverse applications. A novel Adaptive particle swarm optimization (APSO) based approach for parameter determination and feature selection of the SVM, termed APSO+SVM is developed. The illustrating example shows that the classification accuracy of APSO+SVM is higher than other traditional methods of classification, so using APSO+SVM method to classify is feasible and effective.

Purpose – The electric power system is a complex system, whose operating condition may not remain at a constant value. The various contingencies like outage of lines, transformers, generators and sudden increase of load demand or failure of equipments are more common. This causes overloads and system parameters to exceed the limits thus resulting in an insecure system. The purpose of this paper is to enhance the power system security by alleviating overloads on the transmission lines. Design/methodology/approach – Fuzzy logic system (FLS) with particle swarm optimization based optimal power flow approach is used for overload alleviation on the transmission lines. FLS is modeled to find the changes in inertia weight by which new weights are determined and their values are applied to particle swarm optimization (PSO) algorithm for velocity and position updation. Findings – The proposed method is tested and examined on the standard IEEE-30 bus system under base case and increased load conditions at different contingency. This method gives better results in terms of optimum fuel cost and fast convergence under base case and could alleviate the line overloads at different contingency with optimum generation cost, when compared to adaptive particle swarm optimization (APSO) and PSO. Originality/value – FLS is modeled in MATLAB environment. The effectiveness of the proposed method is tested and examined on the standard IEEE-30 bus system and their results are compared with APSO and PSO under MATPOWER environment. The results show that the proposed algorithm is capable of improving the transmission security with optimum generation cost.

Sustainable Green Supply Chain and Logistics Management are crucial to reap environmental and economic wins in today’s complex and competitive global business environment. However, conventional optimization planning techniques can prove inadequate for green supply chain networks. This study proposes a sustainable green supply chain and logistics network that adopts a novel Adaptive Fuzzy Particle Swarm Optimization (AFPSO) method. This study presents a multi-objective mathematical model in combination with Mixed-Integer Linear Programming (MILP) and Multi-Adjacent Descent Traversal Algorithm (MADTA). AFPSO approach bases particle swarm optimization on fuzzy logic to improve efficiency in various conditions. Performance is assessed using parameters such as energy consumption, implementation cost, error values, and enabler applications. Performance assessment is carried out through MATLAB simulations, where the proposed AFPSO-MADTA is compared against Back-Propagation Neural Network (BPNN), the Traditional Particle Swarm Optimization Back-Propagation Neural Network (Traditional PSO-BPNN), and Improved Particle Swarm Optimization Back-Propagation Neural Network (IPSO-BPNN) methods. The results demonstrate that the proposed AFPSO-MADTA approach demonstrates greater energy efficiency, lower costs, higher accuracy, and better sustainability enabler stabilization than traditional optimization methodologies. These findings show the value of AFPSO-MADTA in achieving sustainable supply chain and logistics management. • A new AFPSO-MADTA framework was proposed to sustain green supply chains and logistics. • Implementing Fuzzy logic with Particle Swarm Optimization brought flexibility and better performance in the model. • The framework thus aids in boosting energy efficiency, costs, and sustainability of supply chain networks. • AFPSO-MADTA is better in decreasing the implementation cost and sustain the enablers than traditional approaches. • It presents a strong base for additional research in green logistics and supply chain management.

Network reconfiguration for loss reduction in a distribution system is a very important way to save the electrical energy. This article proposes a hybrid evolutionary algorithm to solve the distribution feeder reconfiguration problem. The algorithm combines a fuzzy adaptive particle swarm optimization with a differential evolution algorithm, called the fuzzy adaptive particle swarm optimization–differential evolution. The fuzzy adaptive particle swarm optimization includes two parts. The first part is fuzzy adaptive binary particle swarm optimization, which determines the status of tie switches (open or closed), and the second part is fuzzy adaptive discrete particle swarm optimization, which determines the sectionalizing switch number. In the proposed algorithm, due to the results of binary particle swarm optimization and discrete particle swarm optimization algorithms that highly depend on the values of their parameters (such as the inertia weight and learning factors), a fuzzy system is employed to adaptively adjust the parameters during the search process. The differential evolution algorithm is combined with the fuzzy adaptive particle swarm optimization algorithm to improve its performance. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and is guaranteed to obtain the global optimization.

The particle swarm optimization (PSO) algorithm is a new evolutionary computation method. It has been proved to be powerful but needs parameters predefined for a given problem by users. An adaptive particle swarm optimization (APSO) algorithm is proposed in this paper. It can adjust parameters automatically in optimization process to find the global optimum. Two power systems, the IEEE 30-bus system and a practical 125-bus power system with 64 control variables, have been tested. Simulation results of the proposed approach, compared with PSO algorithm, show that the APSO algorithm is more efficient in searching global optimization solution.

Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm

Aiming at the control variables of reactive power optimization are discrete, and some parameters in the standard particle swarm optimization (PSO) algorithm need to be predefined by test, so the algorithm's practicability is restricted. For these reasons, an adaptive particle swarm optimization (APSO) algorithm is proposed by the authors. APSO introduces the self-adaptive tuning strategy and boundary constraint conditions can find the global optimal solution and solve the discrete variables. The reactive power optimization results of the standard IEEE-30-bus power system show that APSO is efficient than standard PSO. The global convergence accuracy and convergence stability is obviously improved compared with that of PSO.

Aiming at the precocious convergence problem of particle swarm optimization algorithm, adaptive particle swarm optimization (APSO) algorithm was presented. In this algorithm, the notion of species was introduced into population diversity measure. The species technique is based on the concept of dividing the population into several species according to their similarity. The inertia weight was nonlinearly adjusted by using population diversity information at each iteration step. Velocity mutation operator and position crossover operator were both introduced and the global performance was clearly improved. The APSO algorithm was adapted to search the optimal parameters of support vector machine (SVM) to increase the accuracy of SVM. A novel short-term load forecasting model based on SVM with APSO algorithm (APSO-SVM) is presented. The proposed model was tested on a certain electricity load forecasting problem. The empirical results illustrated that the new APSO-SVM model outperformed SVM, BPNN and regression model and can successfully identify the optimal values of parameters of SVM with the lowest prediction error values in load forecasting. Therefore, this model is efficient and practical during a short-term load forecasting of electric power system.

This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are certified on 6 typical test functions. The simulation results show that the LFAPSO algorithm is obviously more successful than chaotic particle swarm optimization algorithm with adaptive mutation (ACPSO) and adaptive particle swarm optimization (APSO) algorithm in convergence performance and robustness. Furthermore, the results demonstrate the LFAPSO algorithm works better to solve the multidimensional function. The method will be used to different optimization problems such as scheduling problems, training neural networks, image segmentation, etc.

The particle swarm optimization (PSO) algorithm, a new evolutionary computation method, has been proved to be powerful but needs parameters predefined for a given problem. In this paper, a new adaptive particle swarm optimization (APSO) algorithm is proposed and applied to reactive power and voltage control in power systems. The proposed APSO method can adjust parameters automatically in optimization process. The simulation results showthat the APSO algorithm is more efficient in searching global optimization solution compared with the PSO algorithm.

Weapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.

This paper adopts an adaptive inertial weight particle swarm optimization (AIWPSO) algorithm to improve the maximum power point tracking (MPPT) capability for photovoltaic (PV) system under partial shading condition. Partial shading is a common phenomenon in PV generation system, it causes imbalance and decreases for output power of PV array. Under partial shading condition, output characteristics of PV system will change and the P-V characteristic curve contains more than one peak, which makes the conventional algorithm for MPPT is difficult to track the practical MPP. Particle swarm optimization (PSO) algorithm is often used in MPPT under partial shading condition, but PSO algorithm has the disadvantages of low convergence speed and search accuracy. In this paper, AIWPSO algorithm is proposed to solve these problems. In AIWPSO algorithm, a nonlinear dynamic inertia weight factor is introduced into the PSO evolution to improve global searching ability of PSO algorithm. Simulation results for constant partial shading and rapid changing partial shading show that the proposed algorithm can avoid premature convergence effectively and has good global searching capability.