Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance

By Margarita Reyes Sierra, Carlos Artemio Coello Coello. International Conference on Evolutionary Multi-Criterion Optimization, 2005

593 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/88960fb4cb6eef0d353f151f6945264bca333c2b

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Citations in the corpus

The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “Besides NSGA-II, jMetal includes the implementation of a number of both classic and modern multi-objective optimizers; some examples are: SPEA2 [27], PAES [26], OMOPSO [ 29 ], MOCell [30], AbYSS [31], MOEA/D [32], GDE3 [33], IBEA [34], and SMPSO [35].”
Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, background): “ce (IGD) and its variants, the Reactivity measure (React) and its variants, to evaluate the quality of the solutions obtained by these competing algorithms. 1.The inverted generational distance (IGD) [44] is a metric to quantify the performance of a multiobjective optimization algorithm. Let P be th”
Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization (2016, background): “≤ j ≤ n, and j is a multiplication of 3}, w l a M w m j l w a t A s posed by Sierra and Coello, 2005 and formulated as follows: IGD = √∑n i=1 d 2 i n (4.1) where n is the number of true Pareto optimal solutions and di indicates the Euclidean distance between the ith true Pareto optimal solution and…”
Particle swarm optimization algorithm: an overview (2017, background): “In addition, Sierra and Coello (2005) conducted numerical experiments with benchmark objective functions with high dimensions to verify the convergence and effectiveness of the proposed initialization of PSO.”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “Sierra and Coello Coello (2005) implemented a crowding based selection to control the number of leading particles, along with mutation and a e-dominance controlled archive, and thus improved the performance of MO PSO through increased control of particle distribution and the size of the archive.”
SMPSO: A new PSO-based metaheuristic for multi-objective optimization (2009, methodology): “Population Size 100 individuals Selection of Parents binary tournament + binary tournament Recombination simulated binary, pc =0 .9 Mutation polynomial, pm =1 .0/L Parameterization used in OMOPSO [ 14 ]”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “However, as the number of objectives is increased, MOEA/D, GDE3, OMOPSO, and IBEA show signiﬁcant declines in performance.”
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, methodology): “In order to better address feature selection problems, we use another multi-objective PSO to develop a multi-objective feature selection algorithm, CMDPSOFS, which is based on the ideas of crowding, mutation and dominance [41] that has never been applied to feature selection problems.”
Performance metrics in multi-objective optimization (2015, background): “42 Spread: Generalized Spread [43] Δ∗ 43 Success Counting [44] SCC 44 Sum of maximum objective values [45] Smax 45 The adjusted Rand Index [46] 46 The average distance of points to the PF [47] 47 The delineation metric [48] 48 The diversity metric [48] 49 The front Spread [49] FS 50 The front to Set”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, methodology): “Quantitative results are calculated by the inverse generational distance (IGD) proposed by Sierra and Coello Coello [61] over ten runs.”