Abstract
In this chapter, the development of the glowworm swarm optimization (GSO) algorithm is presented. Initially, the basic working principle of GSO is introduced, which is followed by a description of the phases that constitute each cycle of the algorithm. GSO, in its present form, has evolved out of several significant modifications incorporated into the earlier versions of the algorithm. Many ideas were considered in the development process before converging upon the current GSO version.
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Notes
- 1.
In natural glowworms, the brightness of a glowworm’s glow as perceived by its neighbor reduces with increase in the distance between the two glowworms.
- 2.
Let G(V, E) be a graph with vertex set \(V=\{v_1,\ldots , v_n\}\) and edge set \(E=\{(v_i,v_j): v_i, v_j \in V\}\). If E is a set of unordered pairs, then G is said to be an undirected graph. If E is a set of ordered pairs, then G is said to be a directed graph. The graph G is said to be connected if it has a path between each distinct pair of vertices \(v_i\) and \(v_j\) where by a path (of length m) is meant a sequence of distinct edges of G of the form \((v_i,k_1),(k_1,k_2),\ldots (k_m,v_j)\).
- 3.
Movies of some of the simulations presented in this book can be viewed at this link: https://www.youtube.com/watch?v=_vhSu4xBoFs.
- 4.
- 5.
ESN is a recurrent neural network with sparsely connected neurons.
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© 2017 Springer International Publishing AG
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Kaipa, K.N., Ghose, D. (2017). Glowworm Swarm Optimization: Algorithm Development. In: Glowworm Swarm Optimization . Studies in Computational Intelligence, vol 698. Springer, Cham. https://doi.org/10.1007/978-3-319-51595-3_2
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DOI: https://doi.org/10.1007/978-3-319-51595-3_2
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