Particle swarm optimization method in multiobjective problems

By Konstantinos E. Parsopoulos, Michael N. Vrahatis. ACM Symposium on Applied Computing, 2002

712 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9041fec0e46f62d80133d36e294ebb0b1579c92c

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Citations in the corpus

Particle Swarm Methods (2018, methodology): “The donator swarm can be either a neighbor of the k-th swarm in a ring topology scheme as the one described in section “Concept of Neighborhood” or it can be randomly selected [99].”
COVID-19 Outbreak Prediction with Machine Learning (2020, background): “All of these features make PSO different from the GA and other innovative algorithms [61,65,67].”
A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts (2005, methodology): “Previous MOPSO implementations [5,6,7,8,9,10] have all used metrics in objective space (either explicitly or implicitly) in the selection of guides – thus making them susceptible to different scalings in objective space.”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, methodology): “One simple way to approach this problem is to aggregate the multiple objectives into one objective function considering weights that can be fixed or dynamically changing during the optimization process [127].”
A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization (2003, methodology): “PSO has proved to be an efficient optimization method for single objective optimization, and more recently has also shown promising results for solving multiobjective optimization problems [4][5] [6][7].”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “Tabu search (MOTS) [230], the multi-objective scatter search (MOSS) [231], ACO [24], PSO [205], ABC [151], FL [37], ANN, AIS, game theory [219], MOICA [157], memetic algo-”
An effective use of crowding distance in multiobjective particle swarm optimization (2005, methodology): “…Particle Swarm Optimization (MOPSO) [1], Nondominated Sorting Particle Swarm Optimization (NSPSO) [11], the aggregating function for PSO [12], the algorithm of Fieldsend and Singh [5] that uses an unconstrained archive and a data structure called a “dominated tree” and the use of…”
Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, methodology): “2002 Parsopoulos and Vrahatis aggregating functions, scheme similar to lexicographic ordering [26]”
Particle swarm optimization for integer programming (2002, background): “Moreover, it can cope e ciently with Multiobjective Optimization problems [25] and specialized problems, like the `1 norm errors{in{variables problems [23].”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, methodology): “Some researchers used the aggregation approach to solve MOPs [17].”
Swarm Intelligence in Optimization (2008, methodology): “In [137], Parsopoulos and Vrahatis adopted a more traditional weighted-sum approach.”
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, methodology): “The first decomposition based multi-objective PSO algorithm was suggested by Parsopoulos and Vrahatis based on dynamic weighted aggregation [16, 27].”
Handling multiple objectives with particle swarm optimization (2004, methodology): “• The algorithm of Parsopoulos and Vrahatis [26]: Unlike the previous proposals, this algorithm adopts an aggregating function (three types of approaches were implemented: a conventional linear aggregating function, a dynamic aggregating function, and the bang-bang weighted aggregation approach [18]”