Fundamentals of Computational Swarm Intelligence

By Andries Petrus Engelbrecht. 2005

1,949 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/998326e2d16561b974d54a734f2056f61b89f664

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Citations in the corpus

Measuring exploration/exploitation in particle swarms using swarm diversity (2008, background): “Its use in quantifying swarm exploration or exploitation is somewhat intuitive, as a large diversity should directly imply that a large area of the search space is being explored [1].”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, unknown): “(10) and (11) [30,31].”
Harmony Search as a Metaheuristic Algorithm (2010, unknown): “Readers can find more references in [4-9,13-19].”
Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach (2012, background): “Other outstanding features of the PSO algorithm can be pointed as follows [22]–[24]: • It uses the cost function information to navigate search action in the problem space.”
Particle Swarm Model Selection for Authorship Verification (2009, background): “PSO is a bio-inspired search technique that has proved to be very effective in several domains [3].”
An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection (2012, methodology): “PSO, like others swarm algorithms, make use of its own information exchange methods to distribute the best positions found during the algorithm execution [21] [22].”
Social media big data analytics: A survey (2019, unknown): “This technique employs the concept of swarm, such as ants, bees, and wasps, and intelligence based on the structured collection of interacting organisms or agents (Blum & Li, 2008; Engelbrecht, 2006; Fulcher, 2008, pp. 3 – 78).”
Particle Swarm Methods (2018, background): “The theoretical background along with the potential for developing powerful optimization metaheuristics resulted in the emergence of a new category of algorithms under the name of Swarm Intelligence (SI) [12, 34, 62].”
Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food (2014, methodology): “PSO has been successfully utilized in various real world applications (Briza and Naval, 2011; Clerc, 2006; Engelbrecht, 2005; Hu et al., 2011; Nearchou, 2011).”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “the speed of convergence to enable practitioners to choose between fast and potentially low-quality solutions (more exploitation and less exploration) and slow but potentially high-quality solutions (more exploration and less exploitation); see Van den Bergh and Engelbrecht (2006), for example.”
A survey on optimization metaheuristics (2013, background): “Fundamentals of Computational Swarm Intelligence Book [81] introduces the reader to the mathematical models of social insects collective behavior and shows how they can be used in solving optimization problems.”
Hybrid metaheuristics in combinatorial optimization: A survey (2011, background): “Especially in the ﬁelds of evolutionary algorithms and swarm intelligence, the use of pure as well as hybrid metaheuristics for continuous optimization has already a quite long tradition [23, 24, 25].”
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm (2009, unknown): “Since particle swarm optimization (PSO) was introduced [1, 2], many researchers have proposed modifications to the original algorithm (for reviews see [3] and [4]).”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “The evident need of a mutation operator showed that the feasibility rules combined with PSO may cause premature convergence [5].”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “This is inspired by the flocking behavior of birds, which is very simple to simulate and has been found to be quite efficient in handling the single objective optimization (SOO) problems [12] [32].”