Application of improved particle swarm optimization in economic dispatch of power systems

Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been broadly applied to tackle it such as particle swarm optimization. In this dissertation, two improved particle swarm optimization techniques are proposed to solve economic dispatch problems. The first is a hybrid technique with Bat algorithm. Particle swarm optimization as the main optimizer integrates bat algorithm in order to boost its velocity and to adjust the improved solution. The second proposed approach is based on Cuckoo operations. Cuckoo search algorithm is a robust and powerful technique to solve optimization problems. The study investigates the effect of levy flight and random search operation in Cuckoo search in order to ameliorate the performance of the particle swarm optimization algorithm. The two improved particle swarm algorithms are firstly tested on a range of 10 standard benchmark functions and then applied to five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum

This paper presents a hybrid Manta ray foraging—particle swarm optimization algorithm. Manta Ray Foraging Optimization (MRFO) algorithm is a recent algorithm that has a promising performance as compared to other popular algorithms. On the other hand, Particle Swarm Optimization (PSO) algorithm is a well-known and a good performance algorithm. The proposed hybrid algorithm in this work incorporates social interaction and elitism mechanisms from PSO into MRFO strategy. The mechanisms help search agents to determine their new search direction. The proposed algorithm is tested on various dimensions and fitness landscapes of CEC2014 benchmark functions. In solving a real world engineering problem, it is applied to optimize a PD controller for an inverted pendulum system. Result of the benchmark function test is statistically analyzed. The proposed algorithm has successfully improved the accuracy performance for most of the test functions. For optimization of the PD control, result shows that the proposed algorithm has attained a better control performance compared to MRF

An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism

Fruit fly optimization algorithm (FOA) invented recently is a new swarm intelligence method based on fruit fly’s foraging behaviors and has been shown to be competitive with existing evolutionary algorithms, such as particle swarm optimization (PSO) algorithm. However, there are still some disadvantages in the FOA, such as low convergence precision, easily trapped in a local optimum value at the later evolution stage. This paper presents an improved FOA based on the cell communication mechanism (CFOA), by considering the information of the global worst, mean, and best solutions into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that the CFOA outperforms the FOA and the PSO in most of the experiments. Further, the CFOA is applied to optimize the controller for preoxidation furnaces in carbon fibers production. Simulation results demonstrate the effectiveness of the CFOA

Adaptive particle swarm optimization

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity

Handling boundary constraints for particle swarm optimization in high-dimensional search space

Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit. © 2011 Elsevier Inc. All rights reserved