Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

By Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, Sankar Kumar Pal. Information Sciences, 2007

532 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/a175241a94197ce969b50d59dd189e838262e0de

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In Computational Theory and Mathematics

Citations in the corpus

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017, background): “The bigger this value is, the higher the cost of the corresponding feature is; in particular, the value 1 indicates the greatest difficulty in obtaining data for the corresponding feature.”
PSO, an effective tool for harmonics elimination and optimization in multi-level inverters (2009, background): “But, advanced literature recommends adjusting these parameters dynamically [29].”
Self-adaptive learning based particle swarm optimization (2011, methodology): “This idea has been adopted by many works [5,32,56].”
Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food (2014, background): “(42) is called a cognition term that considers a particle0s individual experience; the third term is called asocial term that signifies interaction between particles (Tripathi et al., 2007).”
Particle Swarm Optimization: Technique, System and Challenges (2011, background): “There are some researchers that have develop inertia weight application, such as :[7], [8], [9], [10]”
A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch (2012, methodology): “Three well-known MOPSO algorithms are selected for the performance comparison, which are the multi-objective particle swarm optimization algorithm (CMOPSO) [13], the multi-objective particle swarm with the sigma method (SMOPSO) [21], and the time variant multi-objective particle swarm optimization (”
GSA: A Gravitational Search Algorithm (2009, background): “These algorithms are progressively analyzed or powered by researchers in many different areas [1,3,7,12,25,34].”
Enhancing particle swarm optimization using generalized opposition-based learning (2011, unknown): “[33] proposed a hybrid PSO algorithm with time variant inertia and acceleration coefficients to solve multi-objective optimization problems.”
A review of population-based meta-heuristic algorithm (2013, methodology): “These algorithms apply two approaches to achieve a proper performance [40]: global exploration and local exploitation.”