Glowworm Swarm Optimization : Theory, Algorithms, and by Krishnanand N. Kaipa, Debasish Ghose

By Krishnanand N. Kaipa, Debasish Ghose

This e-book offers a complete account of the glowworm swarm optimization (GSO) set of rules, together with information of the underlying rules, theoretical foundations, set of rules improvement, quite a few purposes, and MATLAB courses for the elemental GSO set of rules. It additionally discusses numerous study difficulties at diversified degrees of class that may be tried through researchers. The generality of the GSO set of rules is clear in its program to various difficulties starting from optimization to robotics. Examples comprise computation of a number of optima, annual crop making plans, cooperative exploration, dispensed seek, a number of resource localization, contaminant boundary mapping, instant sensor networks, clustering, knapsack, numerical integration, fixing fastened aspect equations, fixing structures of nonlinear equations, and engineering layout optimization. The ebook is a precious source for researchers in addition to graduate and undergraduate scholars within the zone of swarm intelligence and computational intelligence and dealing on those topics.

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5, notice that the neighborhood range gets adjusted until the glowworm acquires the number of neighbors that is specified by the parameter n t (=3). 4a shows an initial placement where the glowworm at (0, 0) is isolated. It increases its neighborhood range (Fig. 4b) until it either acquires a set of three neighbors or reaches the maximum range. In Fig. 5a, the glowworm is crowded by a large number of neighbors (|Ni (t)| > n t ) that causes the neighborhood range to shrink until |Ni (t)| = n t .

209] analyze alignment of heading angles of multiple particles using the approach of statistical mechanics. In synchronization of coupled oscillators, a consensus is reached regarding the frequency of oscillation of all agents [200]. The multi-agent rendezvous problem, posed by Ando et al. [6], involves devising local control laws that enable all the members to steer toward, and eventually meet at, a single unspecified location. A variation of this problem may require subgroups of mobile agents to converge at different locations.

At Iteration 2, it is (1 − ρ)2 0 + [1 + (1 − ρ)]γ Jmax , and so on. 12). 2 For all glowworms i co-located at peak-locations X ∗j associated with objective function values J j∗ ≤ Jmax (where, j = 1, 2, . . 1) is used, then i (t) increases or decreases monotonically and asymptotically converges to ∗j = γρ J j∗ . 1), i (t) ≥ 0 always. 19) that is, i (t) increases monotonically. 20) that is, i (t) decreases monotonically. 18) is asymptotically stable. 23), it is clear that the luciferin i (t) of Glowworm i, co-located at a peak-location X ∗j , asymptotically converges to ∗j .

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