Drift Particle Swarm Optimization Algorithm Research Articles

An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision

A novel dynamic cooperative random drift particle swarm optimization algorithm based on entire search history decision (CRDPSO) is reported. At each iteration, the positions and the fitness values of the evaluated solutions in the algorithm are stored by a binary space partitioning tree structure archive, which leads to a fast fitness function approximation. The mutation is adaptive and parameter-less because of the fitness function approximation enhancing the mutation strategy. The dynamic cooperation between the particles by using the context vector increases the population diversity helps to improve the search ability of the swarm and cooperatively searches for the global optimum. The performance of CRDPSO is tested on standard benchmark problems including multimodal and unimodal functions. The empirical results show that CRDPSO outperforms other well-known stochastic optimization methods.

Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem

This paper proposes an improved version of the random drift particle swarm optimization algorithm for solving the economic dispatch problem. The improvement is achieved through adding a crossover operation followed by a greedy selection process while replacing the mean best position of the particles with the personal best position of each particle in the velocity updating equation. The improved algorithm is also augmented with a self-adaption mechanism that eliminates the need for tuning the algorithm parameters based on characteristics of the considered optimization problem. Practical features such as valve point effects, prohibited operating zones, multiple fuel options, and ramp rate limits are considered in the mathematical formulation of the economic dispatch problem. In order to demonstrate the efficacy of the proposed algorithm, five benchmark test systems are utilized. The obtained results showed that the improved random drift particle swarm optimization algorithm is capable of providing superior results compared to the original algorithm and the state of the art techniques proposed in previous literature.

Dynamic Economic Dispatch Problem Integrated With Demand Response (DEDDR) Considering Non-Linear Responsive Load Models

Intelligent implementation of demand response programs (DRPs) not only decreases electricity price in electricity markets, but also improves network reliability. In this paper, the dynamic economic dispatch (DED) problem has been optimally integrated with the incentive-based DRPs. Moreover, mathematical load modeling can be so effective in the load curve estimation with the lowest error. So, economic models of the linear and non-linear responsive loads (power, exponential, and logarithmic) have been developed for time-based and incentive-based DRPs and integrated with DED. Also, a procedure to select the most conservative responsive load model for the load estimation has been presented too. Also, determining the optimal incentive in the incentive-based DRPs is one of the independent system operator’s challenges. In the proposed combined model, the fuel cost is minimized and the optimal incentive is determined simultaneously. Valve-point loading effect, prohibited operating zones, spinning reserve requirements, and the other non-linear practical constraints make the combined problem into a complicated, non-linear, non-smooth, and non-convex optimization problem, which has been solved with a population-based meta-heuristic algorithm namely random drift particle swarm optimization algorithm. The proposed combined model is applied on a ten units test system. Results indicate the practical benefits of the proposed model.