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.

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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

Finding the optimal threshold(s) for an image with a multimodal histogram is described in classical literature as a problem of fitting a sum of Gaussians to the histogram. The fitting problem has been shown experimentally to be a nonlinear minimization problem with local minima. In this paper, we propose to reduce the complexity of the method, by using a parameter-free particle swarm optimization algorithm, called TRIBES which avoids the initialization problem. It was proved efficient to solve nonlinear and continuous optimization problems. This algorithm is used as a "black-box" system and does not need any fitting, thus inducing time gain.

Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm

This brief paper deals with using the Particle Swarm Optimization metaheuristic for optimally sizing switched current (SI) memory cells, namely the class AB grounded gate SI memory cell. Pareto front is generated while optimizing two main conflicting performances: maximizing both the signal to noise ratio and the sampling frequency. SPICE simulation results are presented to validate obtained sizing.

Improving switched current memory cells, performances via the Particle Swarm Optimization technique

Optimisation par essaim particulaire améliorée par hybridation avec un algorithme à estimation de distribution

This paper deals with using MO-TRIBES, an adaptive multiobjective Particle Swarm Optimization algorithm, for optimally sizing CMOS positive second generation Current Conveyors. Pareto front is generated while minimizing parasitic X-port input resistance R X and maximizing current high cut-off frequency f chi . Results obtained using MO-TRIBES are provided and compared to those obtained using the classical MOPSO algorithm. SPICE simulation results are presented to validate obtained sizing.

Yann Cooren

Papers

Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm

Improving switched current memory cells, performances via the Particle Swarm Optimization technique

Optimisation par essaim particulaire améliorée par hybridation avec un algorithme à estimation de distribution

Application of MO-TRIBES to the design of analog electronic circuits