Abstract
As engineering endures to dominate the planet, its application is pervasive in almost all arenas. Thus, the problems associated with engineering optimization takes up a hike. Certain problems may be categorized as single objective optimization (SOO), a problem which has a single criterion to be optimized. In contrary, all modern problems may be classified as multi-objective optimization (MOO) a problem which has two or more objectives to be optimized. The MOO gives the fortuity for selecting the desired solution for the Pareto font where human becomes a decision maker (DM). In extension, most problems are non-deterministic polynomial in nature (NP-hard problems) wherein the obtained solution always converges near to the exact solution. Consequently, algorithms come into picture for solving these NP-hard problems. In this research work, various NP-hard problems which are in association with VLSI design akin wirelength minimization, area minimization, dead space minimization and so forth are delineated and the significant role of biologically inspired algorithms is discussed in a concise manner. The results are compared with other optimization algorithms. The results apparently protrude that bio-inspired algorithms which produce promising results comparatively with other optimization algorithms dominate the arena of algorithms whilst solving problems which are indeed NP-hard. An attempt to analyse the problems with SOO and MOO is attempted which is indeed intriguing.
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Laudis, L.L., Shyam, S., Suresh, V., Kumar, A. (2018). A Study: Various NP-Hard Problems in VLSI and the Need for Biologically Inspired Heuristics. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-10-8636-6_21
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