Wire length optimization of VLSI circuits using IWO algorithm and its hybrid

Circuit World ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Subhrapratim Nath ◽  
Jamuna Kanta Sing ◽  
Subir Kumar Sarkar
Keyword(s):  
Circuit Design ◽  
Heuristic Algorithms ◽  
Vlsi Circuits ◽  
Global Routing ◽  
Vlsi Circuit ◽  
Wire Length ◽  
Invasive Weed ◽  
Routing Problem ◽  
Content Type ◽  
Rectilinear Steiner Tree

Purpose Advancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO). Design/methodology/approach This paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark. Findings This paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits. Practical implications This paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms. Originality/value This paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method.

scholarly journals The Single Row Routing Problem Revisited: A Solution Based on Genetic Algorithms

VLSI Design ◽  
2002 ◽  
Vol 14 (2) ◽  
pp. 123-141 ◽  
Author(s):  
Albert Y. Zomaya ◽  
Roger Karpin ◽  
Stephan Olariu
Keyword(s):  
Genetic Algorithms ◽  
Computer Aided Design ◽  
High Performance ◽  
Printed Circuit Boards ◽  
Heuristic Algorithms ◽  
Classical Problem ◽  
Vlsi Circuits ◽  
Single Chip ◽  
Routing Problem ◽  
Single Row

With the advent of VLSI technology, circuits with more than one million transistors have been integrated onto a single chip. As the complexity of ICs grows, the time and money spent on designing the circuits become more important. A large, often dominant, part of the cost and time required to design an IC is consumed in the routing operation. The routing of carriers, such as in IC chips and printed circuit boards, is a classical problem in Computer Aided Design. With the complexity inherent in VLSI circuits, high performance routers are necessary. In this paper, a crucial step in the channel routing technique, the single row routing (SRR) problem, is considered. First, we discuss the relevance of SRR in the context of the general routing problem. Secondly, we show that heuristic algorithms are far from solving the general problem. Next, we introduce evolutionary computation, and, in particular, genetic algorithms (GAs) as a justifiable method in solving the SRR problem. Finally, an efficient O (nk) complexity technique based on GAs heuristic is obtained to solve the general SRR problem containing n nodes. Experimental results show that the algorithm is faster and can often generate better results than many of the leading heuristics proposed in the literature.

A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up

2018 ◽  
Vol 13 (3) ◽  
pp. 698-717 ◽  
Author(s):  
Masoud Rabbani ◽  
Pooya Pourreza ◽  
Hamed Farrokhi-Asl ◽  
Narjes Nouri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Heuristic Algorithms ◽  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Variable Cost ◽  
Routing Problem ◽  
Content Type ◽  
Simple Genetic Algorithm

Purpose This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). Design/methodology/approach The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Findings A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Originality/value This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

2018 ◽  
Vol 15 (2) ◽  
pp. 254-272 ◽  
Author(s):  
Umamaheswari Elango ◽  
Ganesan Sivarajan ◽  
Abirami Manoharan ◽  
Subramanian Srikrishna
Keyword(s):  
Analysis Of Variance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Population Based ◽  
Maintenance Scheduling ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

A robust possibilistic programming model for production-routing problem in a three-echelon supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Ali Beheshtinia ◽  
Narjes Salmabadi ◽  
Somaye Rahimi
Keyword(s):  
Supply Chain ◽  
Real Life ◽  
Model Parameters ◽  
Pickup And Delivery ◽  
Distribution Centers ◽  
Routing Problem ◽  
Remarkable Feature ◽  
Content Type ◽  
Proposed Model ◽  
Production Routing

Purpose This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered. Design/methodology/approach At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models. Findings The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models. Originality/value This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

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