This paper reviews recent studies on the Particle Swarm Optimization PSO algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

Book review: particle swarm optimization for single objective continuous space problems: A review

Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.

/pdf/a-self-learning-particle-swarm-optimizer-for-global-11pdihwpgn.pdf

A Self-Learning Particle Swarm Optimizer for Global Optimization Problems

Feature selection algorithm based on bare bones particle swarm optimization

Managing convergence and diversity is essential in the design of multiobjective particle swarm optimization (MOPSO) in search of an accurate and well distributed approximation of the true Pareto-optimal front. Largely due to its fast convergence, particle swarm optimization incurs a rapid loss of diversity during the evolutionary process. Many mechanisms have been proposed in existing MOPSOs in terms of leader selection, archive maintenance, and perturbation to tackle this deficiency. However, few MOPSOs are designed to dynamically adjust the balance in exploration and exploitation according to the feedback information detected from the evolutionary environment. In this paper, a novel method, named parallel cell coordinate system (PCCS), is proposed to assess the evolutionary environment including density, rank, and diversity indicators based on the measurements of parallel cell distance, potential, and distribution entropy, respectively. Based on PCCS, strategies proposed for selecting global best and personal best, maintaining archive, adjusting flight parameters, and perturbing stagnation are integrated into a self-adaptive MOPSO (pccsAMOPSO). The comparative experimental results show that the proposed pccsAMOPSO outperforms the other eight state-of-the-art competitors on ZDT and DTLZ test suites in terms of the chosen performance metrics. An additional experiment for density estimation in MOPSO illustrates that the performance of PCCS is superior to that of adaptive grid and crowding distance in terms of convergence and diversity.

Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate System

This paper details the Particle Swarm Optimization (PSO) technique for the optimal design of analog circuits. It is shown the practical suitability of PSO to solve both mono-objective and multiobjective discrete optimization problems. Two application examples are presented: maximizing the voltage gain of a low noise amplifier for the UMTS standard and computing the Pareto front of a bi-objective problem, maximizing the high current cut off frequency and minimizing the parasitic input resistance of a second generation current conveyor. The aptness of PSO to optimize difficult circuit problems, in terms of numbers of parameters and constraints, is shown.

Analog circuit design optimization through the particle swarm optimization technique

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

This paper presents MO-TRIBES, an adaptive multiobjective Particle Swarm Optimization (PSO) algorithm. Metaheuristics have the drawback of being very dependent on their parameter values. Then, performances are strongly related to the fitting of parameters. Usually, such tuning is a lengthy, time consuming and delicate process. The aim of this paper is to present and to evaluate MO-TRIBES, which is an adaptive algorithm, designed for multiobjective optimization, allowing to avoid the parameter fitting step. A global description of TRIBES and a comparison with other algorithms are provided. Using an adaptive algorithm means that adaptation rules must be defined. Swarm's structure and strategies of displacement of the particles are modified during the process according to the tribes behaviors. The choice of the final solutions is made using the Pareto dominance criterion. Rules based on crowding distance have been incorporated in order to maintain diversity along the Pareto Front. Preliminary simulations are provided and compared with the best known algorithms. These results show that MO-TRIBES is a promising alternative to tackle multiobjective problems without the constraint of parameter fitting.

MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm

This brief paper deals with using the particle swarm optimization metaheuristic for optimally sizing CMOS positive second generation current conveyors (CCII+). Both static and dynamic performances are improved. Pareto front is generated while minimizing parasitic X-port input resistance RX and maximizing current high cut off frequency fci. The translinear implementation in CMOS technology is presented. Boundaries of the generated Pareto boarder are 400 Omega and 2 GHz for RX and fci respectively. SPICE simulation results are presented to validate obtained sizing.

Optimizing second generation current conveyors using particle swarm optimization

This chapter presents two ways of improvement for TRIBES, a parameter-free Particle Swarm Optimization (PSO) algorithm. PSO requires the tuning of a set of parameters, and the performance of the algorithm is strongly linked to the values given to the parameter set. However, finding the optimal set of parameters is a very hard and time consuming problem. So, Clerc worked out TRIBES, a totally adaptive algorithm that avoids parameter fitting. Experimental results are encouraging but are still worse than many algorithms. The purpose of this chapter is to demonstrate how TRIBES can be improved by choosing a new way of initialization of the particles and by hybridizing it with an Estimation of Distribution Algorithm (EDA). These two improvements aim at allowing the algorithm to explore as widely as possible the search space and avoid a premature convergence in a local optimum. Obtained results show that, compared to other algorithms, the proposed algorithm gives results either equal or better.

Yann Cooren

Papers

Analog circuit design optimization through the particle swarm optimization technique

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm

Optimizing second generation current conveyors using particle swarm optimization

Initialization and Displacement of the Particles in TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm