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Keywords Drill path optimization • PCB holes drilling process • Cuckoo search algorithm. 1 Introduction. Nature inspired meta-heuristic algorithms are pioneered ...

Cuckoo Search Algorithm for Optimization of Sequence in PCB Holes Drilling Process Wei Chen Esmonde Lim, G. Kanagaraj and S. G. Ponnambalam

Abstract  Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. Most electronic manufacturing industries use computer numerical controlled machines for drilling holes on printed circuit board. To increase PCB manufacturing productivity, a good option is to minimize the drill path route using an optimization algorithm. In order to find the best sequence of operations that achieve the shortest drill path, Cuckoo search algorithm is proposed. The performance of the proposed algorithm is tested and verified with two case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable to efficiently find the optimal route for PCB holes drilling process. Keywords  Drill path optimization  •  PCB holes drilling process  •  Cuckoo search algorithm

1 Introduction Nature inspired meta-heuristic algorithms are pioneered in solving the problems of the modern global optimization, most notably the NP-hard optimization that includes the traveling sales man problem [1, 2]. The power and beauty of modern meta-heuristics comes from the capability of emulating the best feature in nature, specifically biological systems evolved from natural selection over millions of years via two important characteristics, which are selection of the fittest, and adaptation to the environment. Statistically, these features can be interpreted into W. C. E. Lim (*) · G. Kanagaraj · S. G. Ponnambalam  School of Engineering, Monash Univeristy Sunway Campus, Bandar Sunway, Malaysia e-mail: [email protected] G. Kanagaraj e-mail: [email protected] S. G. Ponnambalam e-mail: [email protected]

S. Sathiyamoorthy et al. (eds.), Emerging Trends in Science, Engineering and Technology, Lecture Notes in Mechanical Engineering, DOI: 10.1007/978-81-322-1007-8_18, © Springer India 2012

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two parts of the modern meta-heuristics features: intensification and diversification [3–5]. Intensification intends to search around the best existing solutions and choose the best solutions, while diversification makes sure that the algorithm can explore the search space more efficiently, often via randomization [2]. The cuckoo search (CS) is one of the latest nature inspired meta-heuristic algorithms developed by Xin-She Yang and Suash Deb in [1]. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species). Some host birds can engage direct conflict with the intruding cuckoos. For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Some cuckoo species such as the new world brood parasitic Tapera have evolved in such a way that female parasitic cuckoos are often very specialized in the mimicry in colors and pattern of the eggs of a few chosen host species. CS idealized such breeding behavior, and thus can be applied for various optimization problems. Recent studies show that CS is potentially far more efficient than particle swarm optimizers and genetic algorithms [2]. CS algorithm contains a population of nests or eggs (solution). Each cuckoo lays one egg at a time, and dumps it in a randomly chosen nest. The best nest with high quality of eggs (solutions) will carry over to the next generations. The quality of the solutions is improved by generating a new solution from an existing solution and modifying certain characteristics. The number of available host nests is fixed, and a host can discover an alien egg with a probability Pa. In this case, the host bird can either throw the egg away or abandon the nest so as to build a completely new nest in a new location. Based on these rules, the basic steps of the CS can be summarized and the corresponding pseudo code is shown in Fig. 1. In this paper CS is proposed to find the optimal drill path to minimize the distance of drill tool movement in order to drill all holes at given locations. Fig. 1  Pseudo code of the cuckoo search algorithm

Begin Objective function f(x) Generate initial population of n host nest Evaluate fitness while (t

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