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• W1586157941 abstract "Swarm intelligence is the technological modeling of behaviors of social insects, such as the ant or the honeybee. Although each element comprising swarm intelligence is simple, high grade intelligence emerges when the elements gather to form a swarm. Ant Colony Optimization (Dorigo, M, et al., 1997), which is called ACO, is one of the swarm intelligence and has been attracting much attention recently. The ACO represents a general name of the algorithm inspired by feeding behavior of ants. It has been applied to various combinatorial optimization problems (Ramkumar, A.S. et al., 2006), including the travelling salesman problem (TSP), the floorplanning problem (Luo, R., et al., 2007) and the scheduling problem (Sankar, S.S., et al., 2005). The basic model of the ACO is the ant system (AS) that was proposed by Dorigo et al. (1996), and many ACOs applied to TSP are based on the AS. However, these ACOs require a lot of calculation time, because the optimization process is based on repetitive searches by plural numbers of ants. In this chapter, a novel hardware-oriented ACO (H-ACO) is proposed to achieve high-speed optimization based on mechanism of ACO algorithm. The characteristics of the H-ACO is as follows: (1) all calculations can be performed with only addition, subtraction, and shift operation, instead of the floating point arithmetic and power calculation which are adopted in conventional ACO; (2) a new technique using Look-Up-Table (LUT) is introduced; and (3) in addition to upper and lower limits, benchmarks are set to the pheromone amount. Experiments using benchmark data prove effectiveness of the proposed algorithm. The organization of this chapter is as follows: Section 2 describes the search mechanism of ACO and briefly surveys the ACO research in terms of the computational time. Section 3 explains H-ACO. Section 4 reports the results of computer simulations applied to travelling salesman problem. Section 5 summarizes the chapter." @default.
• W1586157941 created "2016-06-24" @default.
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• W1586157941 date "2008-11-01" @default.
• W1586157941 modified "2023-09-24" @default.
• W1586157941 title "Hardware-oriented Ant Colony Optimization Considering Intensification and Diversification" @default.
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• W1586157941 doi "https://doi.org/10.5772/6346" @default.
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