Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

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.

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Adaptive Particle Swarm Optimization

A study of particle swarm optimization particle trajectories

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

Particle swarm optimization algorithm: an overview

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

Logarithmic spiral phenomena are often appeared in nature. In this letter, we show that a logarithmic spiral can realize an effective strategy regarding the metaheuristics and propose a new two-dimensional metaheuristics called spiral optimization utilizing a feature of the logarithmic spiral. The simulation results for two-dimensional benchmark problems show the effectiveness and the potential of the proposed method. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Primary study of spiral dynamics inspired optimization

Spiral Dynamics Inspired Optimization

The particle swarm optimization (PSO) method is one of the most powerful methods for solving unconstrained and constrained global optimization problems. Little is, however, known about how the PSO method works or finds a globally optimal solution of a global optimization problem when the method is applied to global optimization problems. This paper deals with the analysis of the dynamics of PSO in order to obtain an understanding about how it searches a globally optimal solution and a strategy about how to tune its parameters. While a generalized reduced model of PSO is proposed in order to analyze the dynamics of PSO, the stability analysis is carried out on the basis of both the eigenvalue analysis and some numerical simulations on a typical global optimization problem.

Adaptive particle swarm optimization

In this paper, swarm activity is defined as the root mean square velocity of the particles in particle swarm optimization (PSO). Using numerical experiments, an investigation of the relationship between swarm activity and intensification/diversification during a PSO search, as well as the similarity between swarm activity and temperature in simulated annealing (SA), was conducted. Furthermore, a new method for determining the numerical stability of PSO based on swarm activity was developed. The stability limit for PSO based on the new numerical stability analysis method was compared with the stability limit based on a conventional analytical stability analysis method. Also, using the results of a numerical stability analysis of PSO, the search of conventional PSO methods was examined. From this analysis, issues related to diversification (global search) and intensification (local search) during the search could be explored. Finally, after showing that swarm activity can be controlled using the stable and unstable regions in PSO, a new PSO method that uses swarm activity feedback to control diversification and intensification during a search was proposed. The versatility and search capabilities of the new PSO were examined based on the results of numerical experiments using five typical benchmark problems. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Particle swarm optimization: a numerical stability analysis and parameter adjustment based on swarm activity

This paper proposes an adaptive particle swarm optimization (PSO) algorithm using the information defined as the average absolute value of velocity of all of the particles, which can be used as an index to understand the briskness of all the particles. While a stability analysis of PSO algorithm is carried out on the basis of not only a simplified model but also simple numerical simulations, an adaptive strategy for tuning one of its parameters is introduced so as to follow a given ideal average velocity by feedback control. The feasibility and advantages of the proposed adaptive PSO algorithm are verified through numerical simulations using some typical global optimization problems. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Keiichiro Yasuda

Papers

Primary study of spiral dynamics inspired optimization

Spiral Dynamics Inspired Optimization

Adaptive particle swarm optimization

Particle swarm optimization: a numerical stability analysis and parameter adjustment based on swarm activity

Dynamic parameter tuning of particle swarm optimization