Nature-Inspired-Based Modified Multi-Objective BB-BC Algorithm to Find Near-OGRs for Optical WDM Systems and Its Performance Comparison

Abstract

Multi-objective nature-inspired-based approaches are powerful optimizing algorithms to solve the multiple objectives in NP-complete engineering design problems. This chapter proposes a nature-inspired-based modified multi-objective big bang-big crunch (M-MOBB-BC) optimization algorithm to find the Optimal Golomb rulers (OGRs) in a reasonable timeframe. The OGRs have their important application as channel-allocation algorithm that allow suppression of the four-wave mixing crosstalk in optical wavelength division multiplexing systems. The presented simulation results conclude that the proposed hybrid algorithm is superior to the existing conventional classical algorithms, namely extended quadratic congruence and search algorithm and nature-inspired-based algorithms, namely genetic algorithms, biogeography-based optimization, and simple BB-BC optimization algorithm to find near-OGRs in terms of ruler length, total occupied optical channel bandwidth, bandwidth expansion factor, computation time, computational complexity, and non-parametric statistical tests.

Performance comparison of five metaheuristic nature-inspired algorithms to find near-OGRs for WDM systems

“…the proposed algorithms can explore new search space by the mutation and random walk. A fundamental benefit of using mutation and Lévy flight strategies with nature-inspired algorithms in this paper is their ability to improve its solutions over time, which does not seem in the existing algorithms (Cotta et al 2006;Soliday et al 1995;Robinson 2000;Ayari et al 2010;Dotú and Hentenryck 2005;Bansal 2014Bansal , 2017Bansal and Sharma 2017) to find near-OGRs. Erol and Eksin (2006), inspired by the theories of the evolution of universe; namely, the Big bang and Big crunch theory, developed a metaheuristic algorithm called Big bang-big crunch (BB-BC) optimization algorithm.…”

“…Best known OGRs (Bloom and Golomb 1977;Shearer 1990;Rankin 1993;Colannino 2003;Dollas et al 1998 (Bloom and Golomb 1977;Shearer 1990;Rankin 1993;Colannino 2003;Dollas et al 1998 (Bloom and Golomb 1977;Shearer 1990;Rankin 1993;Colannino 2003;Dollas et al 1998 (continued) distributed OGR project (http://www.distr ibute d.net/ogr) which took several years of calculations on many computers to prove the optimality of the rulers. This subsection is devoted to report the experimental average CPU time taken to find either optimal or near-OGRs by the proposed algorithms and their comparison with the computation time taken by existing algorithms (Shearer 1990;Rankin 1993;Soliday et al 1995;Ayari et al 2010;Bansal 2014Bansal , 2017Bansal and Sharma 2017;Dollas et al 1998; http://www.distr ibute d.net/ogr). Figure 11 illustrates the average CPU time taken by proposed metaheuristic algorithms to find near-OGRs up to 20-marks.…”

“…To date, no efficient algorithm is known for finding the shortest length ruler. The realization of nature-inspired metaheuristic optimization algorithms such as Tabu search (TS) (Cotta et al 2006), Memetic approach (Cotta et al 2006), Genetic algorithms (GAs) (Soliday et al 1995;Robinson 2000;Ayari et al 2010;Dotú and Hentenryck 2005) and its hybridizations (HGA) (Ayari et al 2010), hybrid evolutionary (HE) algorithms (Dotú and Hentenryck 2005), Biogeography based optimization (BBO) (Bansal 2014) and Big bang-big crunch (BB-BC) (Bansal 2017;Bansal and Sharma 2017) in finding relatively good solutions to such NP-complete problems provides a good starting point for algorithms of finding near-OGRs. Therefore, nature-inspired algorithms seem to be very effective solutions for such NP-complete problems.…”