Yann Cooren

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

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.

Optimizing second generation current conveyors using particle swarm optimization

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.

MO-TRIBES, an adaptive multiobjective 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.

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

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.

A Particle Swarm Optimization Technique used for the Improvement of Analogue Circuit Performances

The importance of the analogue part in integrated electronic systems cannot be overstressed. Despite its eminence, and unlike the digital design, the analogue design has not so far been automated to a great extent, mainly due to its towering complexity (Dastidar et al., 2005). Analogue sizing is a very complicated, iterative and boring process whose automation is attracting great attention (Medeiro et al., 1994). The analogue design and sizing process remains characterized by a mixture of experience and intuition of skilled designers (Tlelo-Cuautle & Duarte-Villasenor, 2008). As a matter of fact, optimal design of analogue components is over and over again a bottleneck in the design flow. Optimizing the sizes of the analogue components automatically is an important issue towards ability of rapidly designing true high performance circuits (Toumazou & Lidgey, 1993; Conn et al., 1996). Common approaches are generally either fixed topology ones or/and statistical-based techniques. They generally start with finding a “good” DC quiescent point, which is provided by the skilled analogue designer. After that a simulation-based tuning procedure takes place. However these statistic-based approaches are time consuming and do not guarantee the convergence towards the global optimum solution (Talbi, 2002). Some mathematical heuristics were also used, such as Local Search (Aarts & Lenstra, 2003), Simulated Annealing (Kirkpatrick et al., 1983; Siarry(a) et al., 1997), Tabu Search (Glover, 1989; Glover, 1990), Genetic Algorithms (Grimbleby, 2000; Dreo et al., 2006), etc. However these techniques do not offer general solution strategies that can be applied to problem formulations where different types of variables, objectives and constraint functions are used. In addition, their efficiency is also highly dependent on the algorithm parameters, the dimension of the solution space, the convexity of the solution space, and the number of variables. Actually, most of the circuit design optimization problems simultaneously require different types of variables, objective and constraint functions in their formulation. Hence, the abovementioned optimization procedures are generally not adequate or not flexible enough. In order to overcome these drawbacks, a new set of nature inspired heuristic optimization algorithms were proposed. The thought process behind these algorithms is inspired from

Magnetic resonance image segmentation based on two-dimensional exponential entropy and a parameter free PSO

In this paper, a magnetic resonance image (MRI) segmentationmethod based on two-dimensional exponential entropy (2DEE) and parameterfree particle swarm optimization (PSO) is proposed. The 2DEE technique doesnot consider only the distribution of the gray level information but also takesadvantage of the spatial information using the 2D-histogram. The problem withthis method is its time-consuming computation that is an obstacle in real timeapplications for instance. We propose to use a parameter free PSO algorithmcalled TRIBES, that was proved efficient for combinatorial and non convexoptimization. The experiments on segmentation of MRI images proved that theproposed method can achieve a satisfactory segmentation with a lowcomputation cost.

Improving Switched Current Sigma Delta Modulators' Performances via the Particle Swarm Optimization Technique

This paper presents the optimal design of a switched current sigma delta modulator. The Multi-objective Particle Swarm Optimization technique is adopted to optimize performances of the embryonic cell forming the modulator, that is, a class AB grounded gate switched current memory cell. The embryonic cell was optimized regarding to its main performances such as sampling frequency and signal to noise ratio. The optimized memory cell was used to design the switched current modulator which operates at a 100 MHz sampling frequency and the output signal spectrum presents a 45.75 dB signal to noise ratio.

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

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.

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

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 RX and maximizing current high cut-off frequency fchi. 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.