This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.

Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.

Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

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.

/pdf/particle-swarm-optimization-basic-concepts-variants-and-4h7hr86hk4.pdf

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.

/pdf/adaptive-particle-swarm-optimization-2lys782m7h.pdf

Adaptive Particle Swarm Optimization

Foreword. Introduction. Part 1: Particle Swarm Optimization. Chapter 1. What is a difficult problem? Chapter 2. On a table corner. Chapter 3. First formulations. Chapter 4. Benchmark set. Chapter 5. Mistrusting chance. Chapter 6. First results. Chapter 7. Swarm: memory and influence graphs. Chapter 8. Distributions of proximity. Chapter 9. Optimal parameter settings. Chapter 10. Adaptations. Chapter 11. TRIBES or co-operation of tribes. Chapter 12. On the constraints. Chapter 13. Problems and applications. Chapter 14. Conclusion. Part 2: Outlines. Chapter 15. On parallelism. Chapter 16. Combinatorial problems. Chapter 17. Dynamics of a swarm. Chapter 18. Techniques and alternatives. Further Information. Bibliography. Index.

Particle Swarm Optimization

A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively

A dissipative particle swarm optimization

A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness The testing of two multimodal functions indicates it improves the performance effectively

/pdf/dissipative-particle-swarm-optimization-4i9mmclgzl.pdf

Dissipative particle swarm optimization

An adaptive particle swarm optimization (PSO) on individual level is presented. By analyzing the social model of PSO, a replacement criterion, based on the diversity of fitness between the current particle and the best historical experience, is introduced to maintain the social attribution of swarm adaptively by taking off inactive particles. The testing of three benchmark functions indicates that it improves the average performance effectively.

/pdf/adaptive-particle-swarm-optimization-on-individual-level-44ylnbchu0.pdf

Adaptive particle swarm optimization on individual level

A hybrid particle swarm optimizer with mass extinction, which has been suggested to be an important mechanism for evolutionary progress in the biological world, is presented to enhance the capacity in reaching an optimal solution The tested results of three benchmark functions indicate this method improves the performance effectively

Hybrid particle swarm optimizer with mass extinction

Social cognitive optimization (SCO) for solving nonlinear programming problems (NLP) is presented based on human intelligence with the social cognitive theory (SCT). Experiments comparing SCO with genetic algorithms on some benchmark functions show that the former can produce high-quality solutions efficiently, even with only one learning agent.

Zhi-Lian Yang

Papers

A dissipative particle swarm optimization

Dissipative particle swarm optimization

Adaptive particle swarm optimization on individual level

Hybrid particle swarm optimizer with mass extinction

Social cognitive optimization for nonlinear programming problems