By Chee Peng Lim, Satchidananda Dehuri

Over the prior twenty years, swarm intelligence has emerged as a robust method of fixing optimization in addition to different advanced difficulties. Swarm intelligence versions are encouraged through social behaviours of easy brokers interacting between themselves in addition to with the surroundings, e.g., flocking of birds, education of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions on the colony point are important in attaining complicated goals.

The major objective of this study ebook is to offer a pattern of modern ideas and advances in suggestions and purposes of swarm intelligence. one of the issues lined during this e-book include:

  • particle swarm optimization and hybrid methods
  • ant colony optimization and hybrid methods
  • bee colony optimization, glowworm swarm optimization, and intricate social swarms
  • application of assorted swarm intelligence types to operational making plans of power crops, modelling and keep watch over of nanorobots, class of files, identity of sickness biomarkers, and prediction of gene indications

The e-book is directed to researchers, working towards execs, and undergraduate in addition to graduate scholars of all disciplines who're attracted to bettering their wisdom in concepts and functions of swarm intelligence.

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350. Springer, Heidelberg (1989) 33. : The Parallel Genetic Algorithm as Function Optimizer. Parallel Computing 17(6-7), 619–632 (1991) 34. : Handbook of Evolutionary Computation. Institute of Physics Publishing Ltd, Bristol and Oxford University Press, New York (1997) 35. : A connectionist machine for genetic hillclimbing. Kluwer, Boston (1987) 36. : Evolutionary algorithms in theory and practice. Oxford University Press, Oxford (1996) 37. : Numerical Optimization of Computer Models. John Wiley & Sons, Chichester (1981) 38.

Teodorović and Dell’Orco [10], [14] developed BCO based model for the ride-matching problem. The authors tested the proposed model in the case of ridesharing demand from Trani, a small city in the southeastern Italy. They collected the data regarding 97 travelers demanding for ridesharing, and assumed, for sake of simplicity, that the capacity is 4 passengers for all their cars. Changes of the best discovered objective function values are shown in Figure 6. 3 Routing and Wavelength Assignment in All-Optical Networks Based on the BCO The BCO metaheuristic has been successfully tested [12] in the case of the Routing and Wavelength Assignment (RWA) in All-Optical Networks.

Benatchba et al. [29] applied the MBO algorithm to the Max-Sat problem. Chong et al. [30] applied honey bees foraging model to the job shop scheduling problem. The authors presented experimental results comparing the proposed honeybee colony approach with existing approaches such as ant colony and tabu search. The experimental results showed that the performance of the algorithm is equivalent to ant colony algorithms, Pham et al. [31], [32] proposed population-based search algorithm called the Bees Algorithm (BA).

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