A multi-objective particle swarm optimizer based on decomposition (original) (raw)
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Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition
Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016
Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm Optimization (PSO) has become one of the most popular single-objective optimizers for continuous problems, and recently it has been successfully extended to the multi-objective domain. However, no investigation on the application of PSO within a multi-objective decomposition framework exists in the context of combinatorial optimization. This is precisely the focus of the paper. More specifically, we study the incorporation of Geometric Particle Swarm Optimization (GPSO), a discrete generalization of PSO that has proven successful on a number of single-objective combinatorial problems, into a decomposition approach. We conduct experiments on manyobjective 1/0 knapsack problems i.e. problems with more than three objectives functions, substantially harder than multi-objective problems with fewer objectives. The results indicate that the proposed multi-objective GPSO based on decomposition is able to outperform two version of the wellknow MOEA based on decomposition (MOEA/D) and the most recent version of the non-dominated sorting genetic algorithm (NSGA-III), which are state-of-the-art multi-objective evolutionary approaches based on decomposition.
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.
Multi-Objective Particle Swarm Optimization: An Introduction
Thesmart computing review, 2014
In the real world, reconciling a choice between multiple conflicting objectives is a common problem. Solutions to a multi-objective problem are those that have the best possible negotiation given the objectives. An evolutionary algorithm called Particle swarm optimization is used to find a solution from the solution space. It is a population-based optimization technique that is effective, efficient, and easy to implement. Changes in the particle swarm optimization technique are required in order to get solutions to a multi-objective optimization problem. Therefore, this paper provides the proper concept of particle swarm optimization and the multi-objective optimization problem in order to build a basic background with which to conduct multi-objective particle swarm optimization. Then, we discuss multi-objective particle swarm optimization concepts. Multi-objective particle swarm optimization techniques and some of the most important future research directions are also included.
Multi-objective particle swarm optimization approaches
2008
The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, there are a significant number of multi-objective Particle Swarm Optimization approaches and applications reported in the literature. This chapter aims at providing a review and discussion of the most established results on this field, as well as exposing the most active research topics that can give initiative for future research.
IEEE Access, 2020
Multi-objective optimization has received increasing attention over the past few decades, and a large number of nature-inspired metaheuristic algorithms have been developed to solve multi-objective problems. An external archive is often used to store elite solutions in multi-objective algorithms. Since the archive size is limited, it must be truncated when the number of nondominated solutions exceeds its maximum size. Thus, the archive updating strategy is crucial due to its influence in the performance of the algorithm. However, achieving a fast convergence speed while assuring diversity of the obtained solutions is always a challenging task. In this paper, a novel multi-objective particle swarm optimization algorithm based on a new archive updating mechanism which depends on the nearest neighbor approach, called MOPSONN, is proposed. Two archive updating strategies are adopted to update nondominated solutions in the archive, which are beneficial to accelerate the convergence speed and maintain diversity of the swarm. The performance of MOPOSNN is evaluated on several benchmark problems and compared with seven stateof-the-art multi-objective algorithms, including four multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. The experimental results demonstrate the significant effectiveness of MOPSONN in terms of convergence speed and spread of solutions.
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.
EMOPSO: A Multi-Objective Particle Swarm Optimizer with Emphasis on Efficiency
Lecture Notes in Computer Science
This paper presents the Efficient Multi-Objective Particle Swarm Optimizer (EMOPSO), which is an improved version of a multiobjective evolutionary algorithm (MOEA) previously proposed by the authors. Throughout the paper, we provide several details of the design process that led us to EMOPSO. The main issues discussed are: the mechanism to maintain a set of well-distributed nondominated solutions, the turbulence operator that avoids premature convergence, the constraint-handling scheme, and the study of parameters that led us to propose a self-adaptation mechanism. The final algorithm is able to produce reasonably good approximations of the Pareto front of problems with up to 30 decision variables, while performing only 2,000 fitness function evaluations. As far as we know, this is the lowest number of evaluations reported so far for any multi-objective particle swarm optimizer. Our results are compared with respect to the NSGA-II in 12 test functions taken from the specialized literature.
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...
IJERT-Multi Objective Particle Swarm Optimization: A Survey
International Journal of Engineering Research and Technology (IJERT), 2016
https://www.ijert.org/multi-objective-particle-swarm-optimization-a-survey https://www.ijert.org/research/multi-objective-particle-swarm-optimization-a-survey-IJERTV5IS020467.pdf In this current scenario, choosing any one choice among multiple conflicting objective is became the common problem. These problems are considered to be solved through the decision being made for the given objective is best compromising solution i.e. the solution satisfying all the objectives. Particle swarm optimization is one of meta-heuristic mechanism being used to find solution from the solution space. It belongs to evolutionary algorithm as it is population based optimization technique which figured out to be efficient, effective, flexible and easy implementation. Changes have been made in original particle swarm optimization techniques result in better solutions for multi objective optimization problems. This paper provides the basic known concepts of multi objective optimization as well as of particle swarm optimization. This results in better understanding of the concept of multi objective particle swarm optimization. Here, we also discussed the concepts of multi objective particle swarm optimization, techniques used in multi objective particle swarm optimization, approaches applied in multi objective particle swarm optimization and some of the future related work directions are also being included. Keywords-Multi objective particle swarm optimization(MOPSO); Multi objective optimization problems(MOOPs); Particle swarm optimization(PSO); Pareto optimality; Pareto front.
Dynamic Particle Swarm Optimization to Solve Multi-objective Optimization Problem
Procedia Technology, 2012
Multi-objective optimization problem is reaching better understanding of the properties and techniques of evolutionary algorithms. This paper presents the Dynamic Particle Swarm Optimization algorithm for solving multiobjective PSO in terms of swarm size, topology and search space. In this paper swarm size criteria for dynamic PSO is considered. Experiment conducted for standard benchmark functions of multi-objective optimization problem, which shows the better performance rather the basic PSO.