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Multi-objective optimization for energy and heat-aware VLSI floorplanning using enhanced firefly optimization

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Abstract

The very large scale integration (VLSI) chip design includes a crucial phase called floorplanning. Several nature-inspired metaheuristic algorithms were developed depending on swarm intelligence for VLSI floorplanning. But, the energy consumption and heat generation of floorplanning in VLSI design were not improved using existing metaheuristic algorithms. In order to improve the performance of floorplanning with minimum energy consumption and heat generation, multi-objective firefly optimization-based floorplanning (MOFO-FP) technique is introduced. The main aim of MOFO-FP technique is to reduce the heat generation, space occupied and wire length with the fixed outline constraints for VLSI floorplanning. After constructing the floorplan design, optimization is performed using energy and heat-aware firefly optimization (EHAFO) algorithm. In EHAFO algorithm, the multi-objective function such as heat generation, space occupied and wire length is calculated for efficient floorplanning in VLSI design. Based on the objective functions, the light intensity of each firefly is calculated. From that, the lower brightness fireflies are moved towards higher brightness firefly to identify the optimized position. Lastly, the ranking process carried out to rank the fireflies depends on the intensity to find the optimal tree structure for VLSI floorplanning. This, in turns, proposed MOFO-FP technique which reduces the energy consumption, wire length and heat generation. Experimental evaluation of MOFO-FP technique is carried out with the performance metrics such as wirelength, heat generation, and space consumption compared to the state-of-the-art works. The results are observed that the MOFO-FP technique reduces the space occupied by the circuits with minimum heat generation as well as wire length than the state-of-the-art methods.

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References

  • Anand S, Saravanasankar S, Subbaraj P (2012) Customized simulated annealing based decision algorithms for combinatorial optimization in VLSI floorplanning problem. Comput Optim Appl 52(3):667–689

    Article  MathSciNet  MATH  Google Scholar 

  • Anand S, Saravanasankar S, Subbaraj P (2013) A multiobjective optimization tool for Very Large Scale Integrated nonslicing floorplanning. Int J Circuit Theory Appl 41(9):904–923

    Article  Google Scholar 

  • Chen J, Liu Y, Zhu Z, Zhu W (2017) An adaptive hybrid memetic algorithm for thermal-aware non-slicing VLSI floorplanning. Integr VLSI J 58:245–252

    Article  Google Scholar 

  • Chen X, Wang L, Zomaya AY, Liu L, Shiyan Hu (2015) Cloud computing for VLSI floorplanning considering peak temperature reduction. IEEE Trans Emerg Top Comput 3(4):534–543

    Article  Google Scholar 

  • Chen J, Zhu W, Ali MM (2011) A hybrid simulated annealing algorithm for nonslicing VLSI floorplanning. IEEE Trans Syst Man Cybern 41(4):544–553

    Article  Google Scholar 

  • Cui Z, Sun B, Wang G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distrib Comput 103:42–52

    Article  Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  • Dhiraj SV, Kumar R, Choudhary H (2012) A enhanced algorithm for floorplan design using evolutionary technique. Artif Intell Res 1(2):38–55

    Article  Google Scholar 

  • Feng Y-H, Wang G-G (2018) Binary moth search algorithm for discounted 0–1 knapsack problem. IEEE Access 6:10708–10719

    Article  Google Scholar 

  • Feng Y, Wang G-G, Deb S, Mei Lu, Zhao X-J (2017) Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput Appl 28(7):1619–1634

    Article  Google Scholar 

  • Feng Y, Yang J, Congcong Wu, Mei Lu, Zhao X-J (2018) Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm. Memet Comput 10(2):135–150

    Article  Google Scholar 

  • Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  • Funke J, Hougardy S, Schneider J (2016) An exact algorithm for wirelength optimal placements in VLSI design. Integr VLSI J 52:355–366

    Article  Google Scholar 

  • Guo L, Wang G-G, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402

    Article  Google Scholar 

  • Guohua Wu (2016) Across neighbourhood search for numerical optimization. Inf Sci 329:597–618

    Article  MATH  Google Scholar 

  • Gupta S, Deep K (2019) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112

    Article  Google Scholar 

  • He Y, Yuen SY, Lou Y, Zhang X (2019) A sequential algorithm portfolio approach for black box optimization. Swarm Evol Comput 44:559–570

    Article  Google Scholar 

  • Hoo C-S, Jeevan K, Ganapathy V, Ramiah H (2013) Variable-Order Ant System for VLSI multiobjective floorplanning. Appl Soft Comput 13:3285–3297

    Article  Google Scholar 

  • Hu X-B, Zhang H-L, Zhang C, Zhang M-K, Li H, Leeson MS (2019) A benchmark test problem toolkit for multi-objective path optimization. Swarm Evol Comput 44:18–30

    Article  Google Scholar 

  • Jain L, Singh A (2013) Non slicing floorplan representations in VLSI floorplanning: a summary. Int J Comput Appl 71(15):12–19

    Google Scholar 

  • Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175

    Article  Google Scholar 

  • Laudis LL, Shyam S, Jemila C, Suresh V (2018) MOBA: multi objective bat algorithm for combinatorial optimization in VLSI. Procedia Comput Sci 125:840–846

    Article  Google Scholar 

  • Liang J, Weiwei Xu, Yue C, Kunjie Yu, Song H, Crisalle OD, Boyang Qu (2019) Multimodal multiobjective optimization with differential evolution. Swarm Evol Comput 44:1028–1059

    Article  Google Scholar 

  • Liu Ke, Gong D, Meng F, Chen H, Wang G-G (2017) Gesture segmentation based on a two-phase estimation of distribution algorithm. Inf Sci 394–395:88–105

    Article  MathSciNet  Google Scholar 

  • MCNC Benchmark Netlists for Floorplanning and Placement: https://s2.smu.edu/~manikas/Benchmarks/MCNC_Benchmark_Netlists.html

  • Pandey N, Verma OP, Kumar A (2019) Nature Inspired Power Optimization in smartphones. Swarm Evol Comput 44:470–479

    Article  Google Scholar 

  • Paramasivam S, Athappan S, Natrajan ED, Shanmugam M (2016) Optimization of thermal aware VLSI non-slicing floorplanning using hybrid particle swarm optimization algorithm-harmony search algorithm. Circuits Syst 7:562–573

    Article  Google Scholar 

  • Qi X, Chen S (2017) Fast thermal analysis for fixed-outline 3D floorplanning. Integr VLSI J 59:157–167

    Article  Google Scholar 

  • Rabozzi M, Durelli GC, Miele A, Lillis J, Santambrogio MD (2017) Floorplanning Automation for Partial-Reconfigurable FPGAs via Feasible Placements Generation. IEEE Trans Very Large Scale Integr (VLSI) Syst 25(1):151–164

    Article  Google Scholar 

  • Rizk-Allah RM, El-Sehiemy RA, Deb S, Wang G-G (2017) A Novel Fruit Fly Framework for Multi-Objective Shape Design of Tubular Linear Synchronous Motor. J Supercomput 73(3):1235–1256

    Article  Google Scholar 

  • Rizk-Allah RM, El-Sehiemy RA, Wang G-G (2018) A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution. Appl Soft Comput 63:206–222

    Article  Google Scholar 

  • Shanavas IH, Gnanamurthy RK (2011) Wire length minimization in partitioning and floorplanning using evolutionary algorithms. VLSI Des 2011:1–9

    Article  Google Scholar 

  • Singh A, Jain L (2016) Optimization of VLSI floorplanning problem using a novel genetic algorithm. Int J Comput Sci Inf Secur (IJCSIS) 14(10):937–942

    Google Scholar 

  • Singh RB, Baghel AS, Agarwal A (2016) A review on VLSI floorplanning optimization using metaheuristic algorithms. In: International conference on electrical, electronics, and optimization techniques (ICEEOT), pp 4198–4202

  • Singha T, Dutta HS, De M (2012) Optimization of floor-planning using genetic algorithm. Procedia Technol 4:825–829

    Article  Google Scholar 

  • Sinha A, Soun T, Deb K (2019) Using Karush–Kuhn–Tucker proximity measure for solving bilevel optimization problems. Swarm Evol Comput 44:496–510

    Article  Google Scholar 

  • Sivaranjani P, Senthil Kumar A (2015) Thermal-aware non-slicing VLSI floorplanning using a smart decision-making PSO-GA based hybrid algorithm. Circuits Syst Signal Process 34(11):3521–3542

    Article  Google Scholar 

  • Tighzert L, Fonlup C, Mendil B (2018) A set of new compact firefly algorithms. Swarm Evol Comput 40:92–115

    Article  Google Scholar 

  • Venkatraman S, Sundhararajan M (2017) Optimization for VLSI floorplanning problem by using hybrid ant colony optimization technique. Int J Pure Appl Math 115(6):637–642

    Google Scholar 

  • Wang L (2014) Fast algorithms for thermal-aware floorplanning. J Circuits Syst Comput 23(07):1–14

    Article  Google Scholar 

  • Wang G-G (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput 10(2):151–164

    Article  Google Scholar 

  • Wang G-G, Chud HCE, Mirjalili S (2016a) Three-dimensional path planning for UCAV using an improved bat algorithm. Aerosp Sci Technol 49:231–238

    Article  Google Scholar 

  • Wang G-G, Deb S, Gandomi AH, Alavi AH (2016b) Opposition-based krill herd algorithm with cauchy mutation and position clamping. Neurocomputing 177:147–157

    Article  Google Scholar 

  • Wang G-G, Deb S, Gandomi AH, Zhang Z, Alavi AH (2016c) Chaotic cuckoo search. Soft Comput 20(9):3349–3362

    Article  Google Scholar 

  • Wang G-G, Deb S, Gao X-Z, dos Santos Coelho L (2017a) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J Bio-Inspired Comput 8(6):394–409

    Article  Google Scholar 

  • Wang G-G, Deb S, Zhao X, Cui Z (2018a) A new monarch butterfly optimization with an improved crossover operator. Oper Res 18(3):731–755

    Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH, Dong Y-Q (2015a) A hybrid meta-heuristic method based on firefly algorithm and krill herd. Handbook of research on advanced computational techniques for simulation-based engineering. Springer, Cham, pp 521–540

    Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH (2013a) A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes 42(6):962–978

    Article  MathSciNet  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH (2014a) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462

    Article  MathSciNet  MATH  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH (2014b) Stud krill herd algorithm. Neurocomputing 128:363–370

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH (2015b) Study of Lagrangian and evolutionary parameters in krill herd algorithm. Adapt Hybrid Comput Intell 18:111–128

    MathSciNet  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH, Deb S (2016d) A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput Appl 27(4):989–1006

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH, Deb S (2016e) A multi-stage krill herd algorithm for global numerical optimization. Int J Artif Intell Tools 25(2):15500301–155003017

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH, Gong D (2019) A comprehensive review of krill herd algorithm: variants, hybrids and applications. Artif Intell Rev 51(1):119–148

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH, Hao G-S (2014c) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25(2):297–308

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Yang X-S, Alavi AH (2016f) A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int J Bio-Inspired Comput 8(5):286–299

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Yang X-S, Alavi AH (2016g) A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Eng Comput 31(7):1198–1220

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Zhao X, Chu HCE (2016h) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285

    Article  Google Scholar 

  • Wang G-G, Guo L, Duan H, Wang H (2014d) A new improved firefly algorithm for global numerical optimization. J Comput Theor Nanosci 11(2):477–485

    Article  Google Scholar 

  • Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013b) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanosci 10(10):2312–2322

    Article  Google Scholar 

  • Wang G-G, Guo L, Gandomi AH, Alavi AH, Duan H (2013c) Simulated annealing-based krill herd algorithm for global optimization. Abstr Appl Anal 2013:1–11

    MathSciNet  MATH  Google Scholar 

  • Wang G, Guo L, Gandomi AH, Cao L, Alavi AH, Duan H, Li J (2013d) Lévy-flight krill herd algorithm. Math Probl Eng 2013:1–14

    Google Scholar 

  • Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014e) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871

    Article  Google Scholar 

  • Wang G-G, Mei Lu, Dong Y-Q, Zhao X-J (2016i) Self-adaptive extreme learning machine. Neural Comput Appl 27(2):291–303

    Article  Google Scholar 

  • Wang R, Purshouse RC, Fleming PJ (2013e) Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans Evol Comput 17(4):474–494

    Article  Google Scholar 

  • Wang G-G, Tan Y (2019) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 49(2):542–555

    Article  Google Scholar 

  • Wang H, Yi J-H (2018) An improved optimization method based on krill herd and artificial bee colony with information exchange. Memet Comput 10(2):177–198

    Article  Google Scholar 

  • Wang R, Zhang Q, Zhang T (2016j) Decomposition-based algorithms using pareto adaptive scalarizing methods. IEEE Trans Evol Comput 20(6):821–837

    Article  Google Scholar 

  • Wang R, Zhou Z, Ishibuchi H, Liao T, Zhang T (2018b) Localized weighted sum method for many-objective optimization. IEEE Trans Evol Comput 22(1):3–18

    Article  Google Scholar 

  • Wang G-G, Cai X, Cui Z, Min G, Chen J (2017b) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. In: IEEE transactions on emerging topics in computing, May 2017, pp 1–12

  • Wang G-G, Deb S, Cui Z (2015c) Monarch butterfly optimization. Neural Comput Appl 31:1–20

    Google Scholar 

  • Wang G-G, Deb S, dos Santos Coelho L (2015d) Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int J Bio-Inspired Comput 12(1):1–22

    Article  Google Scholar 

  • Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014f) Chaotic krill herd algorithm. Inf Sci 274:17–34

    Article  MathSciNet  Google Scholar 

  • Wu G, Pedrycz W, Li H, Ma M, Liu J (2016) Coordinated planning of heterogeneous earth observation resources. IEEE Transa Syst Man Cybern Syst 46(1):109–125

    Article  Google Scholar 

  • Wu G, Pedrycz W, Suganthand PN, Li H (2017) Using variable reduction strategy to accelerate evolutionary optimization. Appl Soft Comput 61:283–293

    Article  Google Scholar 

  • Wu G, Shen X, Li H, Chen H, Lin A, Suganthan PN (2018) Ensemble of differential evolution variants. Inf Sci 423:172–186

    Article  MathSciNet  Google Scholar 

  • Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications, pp 169–178

  • Yi J-H, Wang J, Wang G-G (2016) Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Adv Mech Eng 8(1):1–13

    Article  Google Scholar 

  • Zhang J, Wang G (2012) Image matching using a bat algorithm with mutation. Appl Mech Mater 203:88–93

    Article  Google Scholar 

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  1. School of Computing, SASTRA Deemed To Be University, Thanjavur, India

    B. Srinivasan & R. Venkatesan

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  1. B. Srinivasan
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Srinivasan, B., Venkatesan, R. Multi-objective optimization for energy and heat-aware VLSI floorplanning using enhanced firefly optimization. Soft Comput 25, 4159–4174 (2021). https://doi.org/10.1007/s00500-021-05591-x

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