Dynamic Particle Swarm Optimization Algorithm Research Articles

A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

An Improved Hybrid Method Combining Gravitational Search Algorithm With Dynamic Multi Swarm Particle Swarm Optimization

GSA is badly suffering from a slow convergence rate and poor local search ability when solving complex optimization problems. To solve this problem, a new hybrid population-based algorithm is proposed with the combination of dynamic multi swarm particle swarm optimization and gravitational search algorithm (GSADMSPSO). The proposed algorithm has divided the main population of masses into smaller sub-swarms and also stabilizing them by presenting a new neighborhood strategy. Then, by adopting the global search ability of the proposed algorithm, each agent (particle) improves the position and velocity. The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm. In order to evaluate the competences of the proposed algorithm, benchmark functions are employed. The experimental results have been confirmed a better performance of GSADMSPSO as compared with the other gravitational and PSO variants in terms of fitness rate.

Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction

Dynamic multi-objective optimization problems (DMOPs) involve multiple optimization objectives which are in conflict with one another and change over time or environment. A novel dynamic multiple population particle swarm optimization algorithm based on decomposition and prediction (denoted as DP-DMPPSO) is proposed to solve DMOPs. Each objective is optimized by one population and each population shares their information with other populations. The populations evolve independently using a modified particle swarm optimization (PSO). An external archive is adopted to store the non-dominated solutions selected from all populations in the evolutionary process and the archive will be output as the final solution. A mechanism for updating the archive based on the objective space decomposition (DOS) is proposed. In addition, a population prediction mechanism is employed to accelerate the convergence to the true Pareto front. DP-DMPPSO is tested on a set of benchmark problems and compared with several state-of-the-art algorithms. The results show DP-DMPPSO is highly competitive for solving dynamic multi-objective optimization problems.

Dynamic Multiaircraft Cooperative Suppression Interference Array Optimization by Dynamic MOPSO Algorithm

Dynamic multiaircraft cooperative suppression interference array (MACSIA) optimization problem is a typical dynamic multiobjective optimization problem. In this paper, the sum of the distance between each jamming aircraft and the enemy air defense radar network center and the minimum width of the safety area for route planning are taken as the objective functions. The dynamic changes in the battlefield environment are reduced to two cases. One is that the location of the enemy air defense radar is mobile, but the number remains the same. The other is that the number of the enemy air defense radars is variable, but the original location remains unchanged. Thus, two dynamic multiobjective optimization models of dynamic MACSIA are constructed. The dynamic multiobjective particle swarm optimization algorithm is used to solve the two models, respectively. The optimal dynamic MACSIA schemes which satisfy the limitation of the given suppression interference effect and ensure the safety of the jamming aircraft themselves are obtained by simulation experiments. And then verify the correctness of the constructed dynamic multiobjective optimization model, as well as the feasibility and effectiveness of the dynamic multiobjective particle swarm optimization algorithm in solving dynamic MACSIA problem.

A dynamic allocation bare bones particle swarm optimization algorithm and its application

The bare bones particle swarm optimization algorithm is wildly used in different areas. However, this algorithm may suffer from premature convergence by getting trapped in a local optimum when dealing with multimodal functions. To solve this problem, a dynamic allocation bare bones particle swarm optimization (DABBPSO) algorithm is proposed in this work. Particles are divided into two groups before evaluation according to their personal best position. One group is named as main group (MG) and the other one is called the ancillary group (AG). The MG focuses on digging and trying to find the optimal point in the current local optimum. Conversely, the AG aims at exploring the research area and giving the whole swarm more chances to escape from the local optimum. The two groups work together to find the global optimal in the search area. Also, the DABBPSO is applied to a set of well-designed experiments and a set of 0–1 knapsack problems. Finally, the experimental results confirm the optimization ability of the proposed algorithm.

A Hybrid Cellular Swarm Optimization Method for Traffic‐Light Scheduling

With increasing traffic every day, most cities in the world are facing serious traffic problems, such as traffic accidents, congestion and air pollution. Despite the recent improvement of urban infrastructure, reasonable traffic light scheduling still plays an important role in alleviating these traffic problems. It is a great challenge to schedule a huge number of traffic lights efficiently. To solve this problem, we propose a Hybrid cellular swarm optimization method (HCSO) to optimize the scheduling of urban traffic lights. HCSO achieves an efficient and flexible scheduling, which includes the phase timing scheduling and the phase shifting scheduling. To formulate effective solutions for various traffic problems and achieve a globally dynamic scheduling, flexible and concise transition rules based on Cellular automaton (CA) are defined. And the Dynamic cellular particle swarm optimization algorithm (DCPSO) is proposed to find the optimal phase timing scheduling efficiently. Moreover, compared with the differential search algorithm method, the genetic algorithm method, the particle swarm optimizationmethod, the comprehensive learning particle swarm optimization method and the random method in real cases, extensive experiments reveal that HCSO achieves obvious improvements under different traffic conditions.

Monitoring the cohesive damage of the adhesive layer in CFRP double-lapped bonding joint based on non-uniform strain profile reconstruction using dynamic particle swarm optimization algorithm

To well understand the failure mode and monitor the internal damage of the carbon fiber reinforced plastic (CFRP) bonding joint provides guarantee for the safety and reliability of the aerospace. This article obtained the mapping relationship model between the strain profiles change of the adherend and the damage evolution of adhesive layer in the CFRP double-lapped bonding joint by ABAQUS based on the cohesive zone model. Then the Fiber Bragg Grating (FBG) was embedded in CFRP adherend to calculate the reflection spectrum of FBG for reconstructing the adherend strain profile of FBG sensing area with the dynamic particle swarm optimization algorithm. The reconstructed results showed that the maximum error between the simulated strain profile and the reconstructed one under different loads was not exceeding 1.701, revealing good consistency and making it possible to monitor the cohesive damage of adhesive layer based on the non-uniform strain profile reconstruction.

Hypercube optimization based global solution in numerical benchmark and sensor localization

An original learning algorithm for solving global numerical optimization problems is proposed. The proposed algorithm is strong stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The hypercube optimization algorithm includes the initialization and evaluation process, and searching space process. The designed HO algorithm is tested on specific benchmark functions. The comparative performance analysis have made against with other approaches of dynamic weight particle swarm optimization and self-adaptive differential evolution algorithm. Convergence characteristics of self-adaptive differential evolution algorithm has deliver the much better functional value in compare to dynamic weight based particle swarm optimization.

Multi-objective path planning for unmanned surface vehicle with currents effects

This paper investigates the path planning problem for unmanned surface vehicle (USV), wherein the goal is to find the shortest, smoothest, most economical and safest path in the presence of obstacles and currents, which is subject to the collision avoidance, motion boundaries and velocity constraints. We formulate this problem as a multi-objective nonlinear optimization problem with generous constraints. Then, we propose the dynamic augmented multi-objective particle swarm optimization algorithm to achieve the solution. With our approach, USV can select the ideal path from the Pareto optimal paths set. Numerical simulations verify the effectiveness of our formulated model and proposed algorithm.

Arithmetic and parent-centric headless chicken crossover operators for dynamic particle swarm optimization algorithms

This paper conducts an analysis of various strategies for incorporating the headless chicken macromutation operator into a dynamic particle swarm optimization algorithm. Seven variations of the dynamic headless chicken guaranteed convergence particle swarm optimization algorithm are proposed and evaluated on a diverse set of single-objective dynamic benchmark problems. Competitive performance was demonstrated by the headless chicken PSO algorithms when compared to, amongst others, a quantum particle swarm optimization algorithm.

A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization

Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D2PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LODpBest and LODgBest. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is, therefore, referred to as G-DPSO-RE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic-based immune RE operator is developed to facilitate the global best individual ( gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage ( $V_{\mathrm { {dead}}})$ due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed.

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The simulation results show the effectiveness and performance of the proposed method for frequency drop or change in loading.

Multi-objective reliability-based design optimization approach of complex structure with multi-failure modes

To make multi-objective reliability-based design optimization (MORBDO) more effective for complex structure with multi-failure modes and multi-physics coupling, multiple response surface method (MRSM)-based artificial neural network (ANN) (ANN-MRSM) and dynamic multi-objective particle swarm optimization (DMOPSO) algorithm are proposed based on MRSM and MOPSO algorithm. The mathematical model of ANN-MRSM is established by using artificial neural network to fit the multiple response surface function. The DMOPSO algorithm is proposed by designing dynamic inertia weight and dynamic learning factors. The proposed approach is verified by the MORBDO of turbine blisk deformation and stress with respect to fluid–thermal-structure interaction from probabilistic analysis. The optimization design results show that the proposed approach has the promising potentials to improve computational efficiency with acceptable computational precision for the MORBDO of turbine blisk deformation and stress. Moreover, Pareto front curve and a set of viable design values of turbine blisk are obtained for the high-reliability high-performance design of turbine blisk. The presented efforts provide an effective approach for MORBDO of complex structures, and enrich mechanical reliability design theory as well.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

Optimal Generation Rescheduling of Power Systems with Renewable Energy Sources Using a Dynamic PSO Algorithm

1 Abstract: This paper proposes a dynamic particle swarm optimization (PSO) algorithm for optimal generation rescheduling of a power system including renewable energy sources such as the solar and wind energy sources. The algorithm is to minimize total operating costs of this hybrid power system. The proposed dynamic PSO algorithm is one of the standard PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the PSO algorithm to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The dynamic PSO algorithm based optimal generation rescheduling of the power system with and without solar and wind powers is considered on the standard IEEE 30-bus 6-generator 41-transmission line test power system. The numerical results demonstrate the capabilities of the proposed algorithm to generate optimal solutions of the power system considering the renewable energy resources. The comparison with the standard PSO algorithm demonstrates the superiority of the proposed algorithm and confirms its potential to reschedule an optimal generation of the power system including the solar and wind energy sources.

An Enhanced Approach for Parameter Estimation: Using Immune Dynamic Learning Swarm Optimization Based on Multicore Architecture

The identification of physical parameters is crucial for control-system designs, condition monitoring, and fault diagnosis of industrial drive systems. This article brings multicorearchitecture-based parallel computing technology and bioinspired intelligent optimization algorithm insight into designing for system parameter estimation models. In this study, a parallel implementation using an immune-cooperative dynamic learning particle swarm optimization (PSO) algorithm with multicore computation architectures is presented for permanent magnet synchronous machine (PMSM) parameter estimations. Three novel strategies are discussed, all with the purpose of enhancing the dynamic response and fast convergence performance of the designed parameter estimator. The strategies include a dynamic velocity modification strategy, an immune-memory-based searched information preservation mechanism, and an immune-network-based learning operator for PSO. Finally, a proposed method is applied to the parameter estimations of PMSMs as well as parallel running on multicore central processing units (CPUs). The results illustrate that the proposed method can effectively estimate multiple parameters of PMSMs. Moreover, the computational efficiency of the proposed method is greatly enhanced by using multicore parallel computation techniques, which satisfies the required real-time response needed in drive control systems. The proposed method is a generic model that can be applied to other nonlinear parameter identification systems and can assist in operation prediction and state observation of a system.

Dynamic topology multi force particle swarm optimization algorithm and its application

Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.

Task Scheduling Using PSO Algorithm in Cloud Computing Environments

The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.