Dynamic Particle Swarm Optimization Research Articles

Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system

The aim of this paper is to employ fractional order proportional integral derivative &#x0028 FO-PID &#x0029 controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system &#x0028 MLS &#x0029, which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization &#x0028 dPSO &#x0029 technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.

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.

Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization

In this study, dynamic particle swarm optimization was used to develop optimal strategies for two-variable energy management and gear-shifting in hybrid electric vehicles. To perform simulations, six mathematical subsystems, including a spark ignition engine, lithium battery module, traction motor, automatic manual transmission, longitudinal vehicle dynamics, and driver model were constructed. The control strategies for energy management and gear-shifting were modeled using a dynamic particle swarm optimization strategy with three inputs (i.e., battery state-of-charge, engine speed, and demanded torque) and two outputs (i.e., power-split ratio and gear number). The optimization process was divided into six sequential steps. Four different cases were selected for comparison. The results demonstrate that compared with the baseline case, the improvements in the equivalent fuel and energy consumed in the two-variable optimization case were 30.75% and 59.55%, respectively, during the standard European city driving cycle, and 20.54% and 46.95%, respectively, during the federal test procedure cycle. Consequently, the proposed optimization strategy brought superior performance when applied to hybrid energy management and transmission control. The results of a hardware-in-the-loop verification also confirm the effective performance of proposed the online control. In future work, tests using a real vehicle will be conducted.

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.

Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications

Particle Swarm Optimization (PSO) algorithms often face premature convergence problem, specially in multimodal problems as it may get stuck in specific point. In this paper, we have enhanced Dynamic-PSO i.e. and an extention of our earlier research work. This newly proposed algorithm Guided Dynamic-PSO (GDPSO) also targets the particles whose personal best get stuck i.e. their personal best does not improve for fixed number of iterations similar to DPSO, however a new approach is proposed for replacing personal bests of such particles. The replacement of this new personal best is done on the basis of sharing fitness so that better diversity can be provided to avoid the problem. The performance of GDPSO has been compared with PSO and its variants including DPSO over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2015). Results show that the performance of GDPSO is better in comparison with other peer algorithms. Further effectiveness of GDPSO is demonstrated in digital image watermarking. Digital image watermarking schemes primarily focus on providing good tradeoff between imperceptibility and robustness along with reliability in watermarked images produced for wide variety of applications. To support watermarking scheme in achieving this tradeoff, suitable watermark strength is identified in the form of scaling factor using GDPSO for colored images. Results achieved through GDPSO are compared with PSO and other widely accepted variants of PSO over different combination of host and watermark images. Experiment results demonstrate that performance of underline watermarking scheme when used with GDPSO, in terms of imperceptibility and robustness, is better than other variants of PSO.

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.

An efficient multi-unit production planning strategy based on continuous variables

Production planning can significantly enhance the competitiveness and hence plays a crucial role in manufacturing industries. In the production planning problem discussed in this article, a set of processes are available which can be operated at various capacity levels with the production and investment costs being proportional to the production of the processes. The determination of the optimal production plan, in terms of the selection of product to be manufactured, the selection and operating capacity of the manufacturing processes in the presence of multiple resource constraints so as to obtain maximum profit is a combinatorial optimization problem. In this article, we state the limitations of the formulation/strategies employed in literature and propose a multi-unit strategy which utilizes only continuous variables to overcome them. The proposed strategy is generic and demonstrated in the context of production planning in a petrochemical industry that has been previously used to potentially guide the petrochemical industries. The proposed strategy is demonstrated with multiple computational intelligence algorithms viz., artificial bee colony, dynamic neighbourhood learning particle swarm optimizer, multi-population ensemble differential evolution, and sanitized–teaching–learning–based optimization. For the cases discussed in literature, the proposed strategy shows an improvement of up to 12.1% in the profit.

Optimising regulation of aggregated thermostatically controlled loads based on multi‐swarm PSO

Thermostatically controlled loads (TCLs) have been shown to be able to provide regulation service in ancillary service markets. In this work, an online optimisation model of TCLs was established to track the automatic generation control signal. In the optimisation model, the TCLs are regulated with different control commands, and the cluster-based control structure of the TCLs is proposed for implementation. To solve the optimisation problem, a mapping between the temperature setpoint variation and the on/off state of TCLs was established. Hence, the problem was transformed into a 0–1 non-linear programming, which can be solved by the binary dynamic multi-swarm particle swarm optimisation with cooperative learning strategy (DMS-PSO-CLS). Simulation results demonstrate that the binary DMS-PSO-CLS algorithm is an efficient method to solve the optimisation problem. It is promising to control the TCLs individually to serve for the frequency regulation in power grid.

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.

Application of model-based control strategy to hybrid free cooling system with latent heat thermal energy storage for TBSs

This paper explored the application of model predictive control (MPC) technology to the TBSs hybrid free cooling system with latent heat thermal energy storage (LHTES) unit for minimizing the building operational cost without sacrificing temperature requirements. First, the system was briefly introduced and the dynamic thermal process models of building structure and LHTES unit were developed. Then, a hierarchical control structure with dynamic multi-swarm particle swarm optimization was presented to address the dimensional challenge and discontinuities in control variables. Due to the considerable decrease of optimization variable space, the method presented in this paper enables long-term simulation and application in a real controller. Simulations were carried out based on a typical TBS building located in Beijing, China. The total energy consumption of the cooling system and the control quality of indoor air temperature were used as the criteria to evaluate the performance. Compared to a defined baseline case, the optimal control method can achieve significant energy saving, i.e. up to 18%. The impacts of the size of LHTES unit and the type of building structure were discussed, as well. The active and passive heat capacity both played a catalytic role in performance of MPC. Additionally, an uncertainty analysis demonstrated that the proposed approach has strong robustness and can handle quite high errors in forecasting building disturbances from energy consumption level. In summary, the knowledge and use of the plant system and future disturbances make MPC a powerful control tool for TBS buildings for maximizing the use of renewable energy sources.

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.

On the optimal passive formation reconfiguration by using attitude control

The possibility to reconfigure satellite formations with utilization of the drag and the solar radiation pressure has been established with previous works. Passive formation reconfiguration maneuvers may be achieved by controlling the attitude of the satellites and varying the area exposed to the perturbation forces. This paper deals with the development of a simple but effective model to understand the possibilities offered by this kind of maneuvers. Limits and advantages of the presented maneuvers are examined considering different reconfigurations maneuvers among close relative trajectories in Low Earth Orbits, Medium Earth Orbits and Geostationary Orbits. The theoretical investigations are confirmed by numerical simulations where the Inverse Dynamics Particle Swarm Optimization is employed. The relative dynamics is simulated with a high-fidelity orbital simulator considering all the perturbations affecting the spacecrafts orbits and the gravity gradient torque influencing the attitude dynamics. Several test cases are reported to underline all the possibilities offered by the proposed maneuvering strategy.

Inverse dynamics particle swarm optimization applied to constrained minimum-time maneuvers using reaction wheels

The paper deals with the problem of time-optimal spacecraft reorientation maneuvers by means of reaction wheels, with boundary and path constraints. When searching for solutions to optimal attitude-control problems, spacecraft can be easily modeled as controlled by external torques. However, when using actuators such as reaction wheels, conservation of the total angular momentum must be taken into account and the wheel dynamics must be included. A rest-to-rest slew maneuver is considered where an optical sensor cannot be exposed to sources of bright light such as the Earth, the Sun and the Moon. The motion must be constrained to prevent the sensor axis from entering into established keep-out cones. The minimum-time solution is proposed using the Inverse Dynamics Particle Swarm Optimization technique. The attitude and the kinematics of the satellite evolve, leading to the successive attainment of the wheel control input via fixed-step numerical integration. Numerical results are evaluated over different scenarios. It is established that the computation of minimum time maneuvers with the proposed technique leads to near optimal solutions, which fully satisfy all the boundary and path constraints. The ability to converge in a variety of different scenarios always requiring the same computational effort characterizes the proposed technique as a feasible future on-board path-planner.

A Hybrid PSO Based on Dynamic Clustering for Global Optimization

Particle swarm optimization is a population-based global search method, and known to suffer from premature convergence prior to discovering the true global minimizer for global optimization problems. Taking balance of local intensive exploitation and global exploration into account, a novel algorithm is presented in the paper, called dynamic clustering hybrid particle swarm optimization (DC-HPSO). In the method, particles are constantly and dynamically clustered into several groups (sub-swarms) corresponding to promising sub-regions in terms of similarity of their generalized particles. In each group, a dominant particle is chosen to take responsibility for local intensive exploitation, while the rest are responsible for exploration by maintaining diversity of the swarm. The simultaneous perturbation stochastic approximation (SPSA) is introduced into our work in order to guarantee the implementation of exploitation and the standard PSO is modified for exploration. The experimental results show the efficiency of the proposed algorithm in comparison with several other peer algorithms.

A Dynamic Programming-Based Sustainable Inventory-Allocation Planning Problem with Carbon Emissions and Defective Item Disposal under a Fuzzy Random Environment

There is a growing concern that business enterprises focus primarily on their economic activities and ignore the impact of these activities on the environment and the society. This paper investigates a novel sustainable inventory-allocation planning model with carbon emissions and defective item disposal over multiple periods under a fuzzy random environment. In this paper, a carbon credit price and a carbon cap are proposed to demonstrate the effect of carbon emissions’ costs on the inventory-allocation network costs. The percentage of poor quality products from manufacturers that need to be rejected is assumed to be fuzzy random. Because of the complexity of the model, dynamic programming-based particle swarm optimization with multiple social learning structures, a DP-based GLNPSO, and a fuzzy random simulation are proposed to solve the model. A case is then given to demonstrate the efficiency and effectiveness of the proposed model and the DP-based GLNPSO algorithm. The results found that total costs across the inventory-allocation network varied with changes in the carbon cap and that carbon emissions’ reductions could be utilized to gain greater profits.

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.

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

PurposeThe aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem.Design/methodology/approachA modified PSO algorithm is designed.FindingsThe modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm.Originality/valueSome improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

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.