Julien Lepagnot

A survey on optimization metaheuristics

Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences. The classification adopted in this paper differentiates between single solution based metaheuristics and population based metaheuristics. The literature survey is accompanied by the presentation of references for further details, including applications. Recent trends are also briefly discussed.

A New Multiagent Algorithm for Dynamic Continuous Optimization

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.

A multiple local search algorithm for continuous dynamic optimization

Many real-world optimization problems are dynamic (time dependent) and require an algorithm that is able to track continuously a changing optimum over time. In this paper, we propose a new algorithm for dynamic continuous optimization. The proposed algorithm is based on several coordinated local searches and on the archiving of the optima found by these local searches. This archive is used when the environment changes. The performance of the algorithm is analyzed on the Moving Peaks Benchmark and the Generalized Dynamic Benchmark Generator. Then, a comparison of its performance to the performance of competing dynamic optimization algorithms available in the literature is done. The obtained results show the efficiency of the proposed algorithm.

Using a novel parallel genetic hybrid algorithm to generate and determine new zeolite frameworks

Abstract Zeolite structure determination and zeolite framework generation are not new problems but due to the increasing computer power, these problems came back and they are still a challenge despite the recent progress in terms of structural resolution from X-rays and electron diffraction. The infinite number of potential solutions and the computational cost of this problem make the use of metaheuristics significant for this problem. In this paper, we propose a new approach based on parallel genetic hybrid algorithm for zeolites using a modified modelization of the objective function to find hypothetical zeolite structures, close to the thermodynamic feasibility criterion. A population made of random atoms is initialized. At each generation, a crossover operator and a mutation heuristic are applied. Each individual of the population generates a potential zeolitic structure by applying the symmetry operators of a given crystallographic space group. This structure is evaluated with our objective function. From the unit cell parameters and the number of T atoms in the asymmetric unit, 6 possible zeolitic interesting structures have been found.

Performance Analysis of MADO Dynamic Optimization Algorithm

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm for solving dynamic problems is studied. This algorithm, called MADO, is analyzed using the Moving Peaks Benchmark, and its performances are compared to those of competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of MADO, even in multimodal environments.

PSO-2S optimization algorithm for brain MRI segmentation

In image processing, finding the optimal threshold(s) for an image with a multimodal histogram can be done by solving a Gaussian curve fitting problem, i.e. fitting a sum of Gaussian probability density functions to the image histogram. This problem can be expressed as a continuous nonlinear optimization problem. The goal of this paper is to show the relevance of using a recently proposed variant of the Particle Swarm Optimization (PSO) algorithm, called PSO-2S, to solve this image thresholding problem. PSO-2S is a multi-swarm PSO algorithm using charged particles in a partitioned search space for continuous optimization problems. The performances of PSO-2S are compared with those of SPSO-07 (Standard Particle Swarm Optimization in its 2007 version), using reference images, i.e. using test images commonly used in the literature on image segmentation, and test images generated from brain MRI simulations. The experimental results show that PSO-2S produces better results than SPSO-07 and improves significantly the stability of the segmentation method.

A Dynamic Multi-Agent Algorithm applied to challenging benchmark problems

Many real-world optimization problems are dynamic (time dependent) and require an algorithm that is able to continuously track a changing optimum over time. In this paper, we investigate a recently proposed algorithm for dynamic continuous optimization, called MLSDO (Multiple Local Search algorithm for Dynamic Optimization). MLSDO is based on several coordinated local search agents and on the archiving of the optima found over time. This archive is used when a change occurs in the objective function. The performance of the algorithm is evaluated on the set of benchmark functions provided for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems.

Comparison of Two Diversification Methods to Solve the Quadratic Assignment Problem

The quadratic assignment problem is one of the most studied NP-hard problems. It is known for its complexity which makes it a good candidate for the parallel design. In this paper, we propose and analyze two parallel cooperative algorithms based on hybrid iterative tabu search. The only difference between the two approaches is the diversification methods. Through 15 of the hardest well-known instances from QAPLIB benchmark, our algorithms produce competitive results.

Hybrid Imperialist Competitive Algorithm with Simplex Approach: Application to Electric Motor Design

Imperialist competitive algorithm (ICA) is a population based metaheuristic inspired from imperialistic competition among empires. In order to improve its performances, we propose to hybridize ICA with the Nelder-Mead simplex method. The simplex algorithm is run if a stagnation criterion is satisfied, in order to help ICA escape local optima and to improve its intensification capabilities. The proposed hybrid ICA-simplex algorithm, called ICAS, is first analyzed and compared to the unmodified ICA and two well-known algorithms using the benchmark functions provided during the 2005 IEEE Congress on Evolutionary Computation. The results show the efficiency of the proposed hybrid algorithm. Then, it is used to optimize the design of a permanent-magnet machine used to motorize an electric scooter. The solution found by ICAS is shown to be better than those of several well-known metaheuristics.

Brain cine-MRI registration using MLSDO dynamic optimization algorithm

In this chapter, we propose to use a dynamic optimization algorithm to assess the deformations of the wall of the third cerebral ventricle in the case of a brain cine-MRI. In this method, a segmentation process is applied to a 2D+t cine-MRI sequence to detect the contours of a region of interest (i.e. lamina terminalis). Then, successive segmented contours are matched using a global alignment procedure, followed by a registration process. This registration process consists in optimizing an objective function that can be considered as a dynamic function. Thus, a dynamic optimization algorithm, called MLSDO, is used to solve the registration problem. The results obtained by MLSDO are compared to those of several well-known static optimization algorithms. This comparison shows the efficiency of MLSDO, and the relevance of using a dynamic optimization algorithm to solve this kind of problems.