An Improved Artificial Weed Colony for Continuous Optimization

Rahimi, Asgar Bradaran; Ahangaran, Milad; Ramezani, Pezhman; Kashkooli, Tarlan

doi:10.1109/ems.2011.30

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Invasive weed optimization (IWO) (Mehrabian et al , 2006; Rad and Lucas, 2007; Rahimi et al , 2011; Affolter et al , 2014; Ren et al , 2016) is a numerical stochastic algorithm inspired from the colonizing nature of weeds. Weed is any plant, which with its robust and troublesome nature, grows to populate regions where it is not wanted and are characterized by vigor and invasive habits which make them a serious threat to desirable, cultivated plants.…”

Section: Preliminariesmentioning

confidence: 99%

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

Nath

Sing

Sarkar

2021

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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.

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Section: Preliminariesmentioning

confidence: 99%

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

Nath

Sing

Sarkar

2021

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“…Recently, Rahimi et al (2011) proposed an improved invasive weed optimisation (IIWO) algorithm which attempted to assign different values of standard deviations to weeds depending on their fitness values in any iteration. Although IIWO works better than the standard IWO, it is still suffering from getting stuck on local optima.…”

Section: Eiwo Algorithm For Constrained Optimisation Problemmentioning

confidence: 99%

Constrained optimisation and robust function optimisation with EIWO

Ramezani

Ahangaran

Yang

2013

IJBIC

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A robust variant of invasive weed optimisation (IWO) algorithm, called enhanced invasive weed optimisation (EIWO) algorithm, is proposed in this paper for the optimisation of constrained benchmark problems. Enjoying the ecological behaviour of colonising weeds, IWO has demonstrated its ability in solving different optimisation problems. Since making a proper balance between these two components is essential, especially to cope with constraint optimisation problems, two new rules are added to the algorithm to improve its performance. The first rule is utilising principles of social standard deviation as proposed in social harmony search (SHS) algorithm. The second rule is utilised to prevent the algorithm to get stuck on local optima. Finally, for constraint handling, three simple heuristic rules of Deb are utilised. The robustness and effectiveness of the proposed method are tested on many constrained benchmark problems and compared against those of state-of-the-art algorithms.Reference to this paper should be made as follows: Ramezani, P., Ahangaran, M. and Yang, X-S. (2013) 'Constrained optimisation and robust function optimisation with EIWO', Int. J.

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Application of Natured-Inspired Algorithms for the Solution of Complex Electromagnetic Problems

Donelli

2017

Handbook of Research on Soft Computing and Nature-Inspired Algorithms

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In the last decade nature-inspired Optimizers such as genetic algorithms (GAs), particle swarm (PSO), ant colony (ACO), honey bees (HB), bacteria feeding (BFO), firefly (FF), bat algorithm (BTO), invasive weed (IWO) and others algorithms, has been successfully adopted as a powerful optimization tools in several areas of applied engineering, and in particular for the solution of complex electromagnetic problems. This chapter is aimed at presenting an overview of nature inspired optimization algorithms (NIOs) as applied to the solution of complex electromagnetic problems starting from the well-known genetic algorithms (GAs) up to recent collaborative algorithms based on smart swarms and inspired by swarm of insects, birds or flock of fishes. The focus of this chapter is on the use of different kind of natured inspired optimization algorithms for the solution of complex problems, in particular typical microwave design problems, in particular the design and microstrip antenna structures, the calibration of microwave systems and other interesting practical applications. Starting from a detailed classification and analysis of the most used natured inspired optimizers (NIOs) this chapter describes the not only the structures of each NIO but also the stochastic operators and the philosophy responsible for the correct evolution of the optimization process. Theoretical discussions concerned convergence issues, parameters sensitivity analysis and computational burden estimation are reported as well. Successively a brief review on how different research groups have applied or customized different NIOs approaches for the solution of complex practical electromagnetic problem ranging from industrial up to biomedical applications. It is worth noticed that the development of CAD tools based on NIOs could provide the engineers and designers with powerful tools that can be the solution to reduce the time to market of specific devices, (such as modern mobile phones, tablets and other portable devices) and keep the commercial predominance: since they do not require expert engineers and they can strongly reduce the computational time typical of the standard trial errors methodologies. Such useful automatic design tools based on NIOs have been the object of research since some decades and the importance of this subject is widely recognized. In order to apply a natured inspired algorithm, the problem is usually recast as a global optimization problem. Formulated in such a way, the problem can be efficiently handled by natured inspired optimizer by defining a suitable cost function (single or multi-objective) that represent the distance between the requirements and the obtained trial solution. The device under development can be analyzed with classical numerical methodologies such as FEM, FDTD, and MoM. As a common feature, these environments usually integrate an optimizer and a commercial numerical simulator. The chapter ends with open problems and discussion on future applications.

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An Improved Artificial Weed Colony for Continuous Optimization

Cited by 3 publications

References 9 publications

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

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

Constrained optimisation and robust function optimisation with EIWO

Application of Natured-Inspired Algorithms for the Solution of Complex Electromagnetic Problems

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