EMOPSO: A Multi-Objective Particle Swarm Optimizer with Emphasis on Efficiency (original) (raw)

Evolutionary multi-objective optimization of Particle Swarm Optimizers

Proc. of the 2007 IEEE Congress on Evolutionary Computation, 2007

One issue in applying Particle Swarm Optimization (PSO) is to find a good working set of parameters. The standard settings often work sufficiently but don't exhaust the possibilities of PSO. Furthermore, a trade-off between accuracy and computation time is of interest for complex evaluation functions. This paper presents results for using an EMO approach to optimize PSO parameters as well as to find a set of trade-offs between mean fitness and swarm size. It is applied to four typical benchmark functions known from literature. The results indicate that using an EMO approach simplifies the decision process of choosing a parameter set for a given problem.

Towards a More Efficient Multi-Objective Particle Swarm Optimizer

Theory and Practice

This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this approach is to combine the high convergence rate of the PSO algorithm with a local search approach based on scatter search, in order to have the main advantages of these two types of techniques. We propose a new leader selection scheme for PSO, which aims to accelerate convergence by increasing the selection pressure. However, this higher selection pressure reduces diversity. To alleviate that, scatter search is adopted after applying PSO, in order to spread the solutions previously obtained, so that a better distribution along the Pareto front is achieved. The proposed approach can produce reasonably good approximations of multi-objective problems of high dimensionality, performing only 4,000 fitness function evaluations. Test problems taken from the specialized literature are adopted to validate the proposed hybrid approach. Results are co...

Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art

International Journal of Computational Intelligence Research, 2006

The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer (mainly when dealing with continuous search spaces) has motivated researchers to extend the use of this bio-inspired technique to other areas. One of them is multi-objective optimization. Despite the fact that the first proposal of a Multi-Objective Particle Swarm Optimizer (MOPSO) is over six years old, a considerable number of other algorithms have been proposed since then. This paper presents a comprehensive review of the various MOPSOs reported in the specialized literature. As part of this review, we include a classification of the approaches, and we identify the main features of each proposal. In the last part of the paper, we list some of the topics within this field that we consider as promising areas of future research.

An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems

International Journal of Computer and Information Engineering, 2018

Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems.

Multi-Objective Particle Swarm Optimizers: An Experimental Comparison

Lecture Notes in Computer Science, 2009

Particle Swarm Optimization (PSO) has received increased attention in the optimization research community since its first appearance. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOP-SOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which version of MOPSO shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately.

A Multi-objective Particle Swarm Optimizer Hybridized with Scatter Search

Lecture Notes in Computer Science, 2006

This paper presents a new multi-objective evolutionary algorithm which consists of a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main idea of the approach is to combine the high convergence rate of the particle swarm optimization algorithm with a local search approach based on scatter search. We propose a new leader selection scheme for PSO, which aims to accelerate convergence. Upon applying PSO, scatter search acts as a local search scheme, improving the spread of the nondominated solutions found so far. Thus, the hybrid constitutes an efficient multi-objective evolutionary algorithm, which can produce reasonably good approximations of the Pareto fronts of multi-objective problems of high dimensionality, while only performing 4,000 fitness function evaluations. Our proposed approach is validated using ten standard test functions commonly adopted in the specialized literature. Our results are compared with respect to a multi-objective evolutionary algorithm that is representative of the state-of-the-art in the area: the NSGA-II.

A Multi-Objective Evolutionary Hybrid Optimizer

2005

A new hybrid multi-objective, multivariable optimizer utilizing Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Differential Evolution (NSDE), and Multi-Objective Particle Swarm (MOPSO) has been created and tested. The optimizer features automatic switching among these algorithms to expedite the convergence of the optimal Pareto front in the objective function(s) space. The ultimate goal of using such a hybrid optimizer is to

Smpso: A new pso-based metaheuristic for multi-objective optimization

… intelligence in miulti- …, 2009

In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the nondominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed.

A scalability study of multi-objective particle swarm optimizers

2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013

Particle swarm optimization (PSO) is a well-known optimization technique originally proposed for solving singleobjective, continuous optimization problems. However, PSO has been extended in various ways to handle multi-objective optimization problems (MOPs). The scalability of multi-objective PSO algorithms as the number of sub-objectives increases has not been well examined; most observations are for two to four objectives. It has been observed that the performance of multiobjective optimizers for a low number of sub-objectives can not be generalized to problems with higher numbers of sub-objectives. With this in mind, this paper presents a scalability study of three well-known multi-objective PSOs, namely vector evaluated PSO (VEPSO), optimized multi-objective PSO (oMOPSO), and speed-constrained multi-objective PSO (SMPSO) with up to eight sub-objectives. The study indicates that as the number of subobjectives increases, SMPSO scaled the best, oMOPSO scaled the worst, while VEPSO's performance was dependent on the knowledge transfer strategy (KTS) employed, with parent centric recombination (PCX) based approaches scaling consistently better. 1+