An Improved Multi-Objective Particle Swarm Optimization (original) (raw)
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This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
Multi Objective Particle Swarm Optimization
International Journal of Engineering Research and, 2017
several optimization techniques are proposed in artificial intelligence. This paper we contrast performance of Swarm Intelligence based PSO search strategy to optimize the multiple objective functions. Experimental analysis also demonstrated the effect of the inertia weight for multiple objective functions in the algorithm. And optimize time for all particles are detected and calculated by Particle Swarm Optimization.
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.
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.
Improved Multi-Objective PSO Algorithm for Optimization Problems
Some Particle Swarm Optimization (PSO) algorithm have been used to solve Multi-Objective Optimization Problems (MOP) and have achieved good results. But finding a good convergence and distribution of solutions near the Pareto optimal front in little computational time is still a hard work especially for some complex functions. This paper introduces an improved multi-objective PSO algorithm. It is called Strength Pareto Particle Swarm Optimization algorithm(SPPSO) which uses the ranking and sharing strategies of Strength Pareto Evolutionary Algorithm II (SPEA2). The hyper-volume metric (Zitzler 1999) is introduced to evaluate overall performance of the obtained solutions. Simulation results on five difficult test problems show that the proposed algorithm is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to CMOPSO.
Engineering Optimization, 2008
Multi-objective problems with conflicting objectives cannot be effectively solved by aggregation-based methods. The answer to such problems is a Pareto optimal solution set. Due to the difficulty of solving multi-objective problems using multi-objective algorithms and the lack of enough expertise, researchers in different fields tend to aggregative objectives and use single-objective algorithms. This work is a seminal attempt to propose the use of multi-objective algorithms in the field of hand posture estimation. Hand posture estimation is a key step in hand gesture recognition, which is a part of an overall attempt to make human-computer interaction more like human face-to-face communication. Hand posture estimation is first formulated as a bi-objective problem. A modified version of Multi-Objective Particle Swarm Optimisation (MOPSO) is then proposed to approximate the Pareto optimal font of 50 different postures. The main motivation of integrating a new operator (called Evolutionary Population Dynamics-EPD) in MOPSO is due to the nature of hand posture estimation problems in which parameters should not be tuned in a same manner since they show varied impacts on the objectives. EPD allows randomising different parameters in a solution and provides different exploratory behaviours for the parameters of an optimisation algorithm rather than each individual solution. The MOPSO algorithm is equipped with a mechanism to randomly re-initialise poor particles around the optimal solutions in the archive. The improved MOPSO is tested on ZDT and CEC2009 test functions and compared with the standard MOPSO, NSGA-II, and MOEA/D. The results show that the proposed MOPSO (MOPSO+EPD) significantly outperforms MOPSO on the majority of test functions in terms of both convergence and coverage. MOPSO+EPD also approximates well-distributed Pareto optimal fronts for most of the postures considered in this work. The post analysis of the results is conducted to understand the relationship between the parameters and objectives of this problem (design principals) for the first time in the literature as well.
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.
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.