Titlebook: Handbook of Swarm Intelligence; Concepts, Principles Bijaya Ketan Panigrahi,Yuhui Shi,Meng-Hiot Lim Book 2011 Springer-Verlag Berlin Heidel

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Integral-Controlled Particle Swarm Optimizationn dealing with multi-modal high-dimensional problems. To overcome this shortcoming, two integral controllers are incorporated into the methodology of PSO, and the integral-controlled particle swarm optimization (ICPSO) is introduced. Due to the additional accelerator items, the behavior of ICPSO is

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Particle Swarm Optimization for Markerless Full Body Motion Capturearch the true body configurations for any search strategy. In this chapter, we apply the stochastic Particle Swarm Optimization (PSO) algorithm to full body pose estimation problem. Our method fits an articulated body model to the volume data reconstructed from multiple camera images. Pose estimatio

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An Adaptive Multi-Objective Particle Swarm Optimization Algorithm with Constraint Handlingcalled Constrained Adaptive Multi-objective Particle Swarm Optimization (CAMOPSO). CAMOPSO is based on the Adaptive Multi-objective Particle Swarm Optimization (AMOPSO) method proposed in [1]. As in AMOPSO, the inertia and the acceleration coefficients are determined adaptively in CAMOPSO, while a p

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A Multi-objective Resource Assignment Problem in Product Driven Supply Chain Using Quantum Inspired duct driven supply chain. The problem has been mathematically modeled as a multi-objective optimization problem with the objectives of profit, quality, ahead time of delivery and volume flexibility. In this research, product characteristics have been associated with the design requirements of a supp

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Honeybee Optimisation – An Overview and a New Bee Inspired Optimisation Schemeis class of algorithms is based on the behaviour of honeybees. Current algorithms are based on either of two principles: foraging or mating. Algorithms based on mating utilize the behavioral principles of polyandry found in honey bees and algorithms based on foraging apply the principles of collecti

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Parallel Approaches for the Artificial Bee Colony Algorithm three parallel models: master-slave, multi-hive with migrations, and hybrid hierarchical. Extensive experiments were done using three numerical benchmark functions with a high number of variables. Statistical results indicate that intensive local search improves the quality of solutions found and,