Application of a novel IWO to the design of encoding sequences for DNA computing

Zhang, Xuncai; Wang, Yanfeng; Cui, Guangzhao; Niu, Ying; Xu, Jin

doi:10.1016/j.camwa.2008.10.038

Cited by 81 publications

(26 citation statements)

References 12 publications

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“…Following this tradition, in 2006, Mehrabian and Lucas proposed the Invasive Weed Optimization (IWO) [13], a derivative-free, metaheuristic algorithm, mimicking the ecological behavior of colonizing weeds. Since its inception, IWO has found successful applications in many practical optimization problems like optimization and tuning of a robust controller [13 ], optimal positioning of piezoelectric actuators [14], developing a recommender system [15], antenna configuration optimization [16], design of E-shaped MIMO Antenna [17], design of encoding sequences for DNA computing [18], and design of compact U-array MIMO antenna design [19].…”

Section: Invasive Weed Optimization (Iwo)mentioning

confidence: 99%

Automatic shell clustering using a metaheuristic approach

Pal

Basak

Das

et al. 2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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Section: Invasive Weed Optimization (Iwo)mentioning

confidence: 99%

Automatic shell clustering using a metaheuristic approach

Pal

Basak

Das

et al. 2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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“…Lately a derivative-free, meta-heuristic optimization algorithm named as Invasive Weed Optimization (IWO) is proposed [17] which imitate the ecological behavior of the colonizing weeds. Successfully utilization of IWO has been found since then in different optimization problems like tuning of a robust controller [17], optimal positioning of piezoelectric actuators [18], designing an E-shaped MIMO antenna [20], designing the encoding sequences for DNA computing [21], developing a recommender system [22] and studying electricity market dynamics [23]. PSO was introduced by Kennedy and Eberhart in 1995 [24].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Ahmed¹,

Amin²

2012

IJCA

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In this paper, two evolutionary algorithms-Invasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a derivative-free and flexible optimizer which is powered by the behavior of organism, such as bird flocking. Eigen-value based objective function is considered for the tuning of PSSs to enhance system damping of electromechanical mode. The performance of proposed IWO-based PSS and PSO-based PSS is tested and demonstrated under different disturbances for a four machine example power system. The Eigen value analysis and non-linear time domain simulation results shows that both IWO-based PSS and PSO-based design can successfully damp out the oscillations and thus improve the stability of the system. However, the abilities like faster convergence and greater shifting of critical modes to the left of s-plane keeps the choice of IWO based design in front of PSO based design for the system under consideration. General TermsPower system stability, Multi-machine system, Optimization.

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“…The IWO algorithm was proposed by Mehrabian and Lucas [1] in 2006, and since then, it has been successfully utilized in different practical optimization problems such as optimal positioning of piezoelectric actuators [4], demanding a recommender system [5], Studying electricity market dynamics [6], Design of an E shaped MIMO antenna [7] and encoding sequences for DNA computing [8].…”

Section: Invasive Weed Optimization Algorithm For Fjsspmentioning

confidence: 99%

Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Optimization

Mekni¹,

Fayech²

2015

IJSC

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Recently, many studies are carried out with inspirations from ecological phenomena for developing optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it isable to find the optimal and best-known solutions on the instances studied.

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Application of a novel IWO to the design of encoding sequences for DNA computing

Cited by 81 publications

References 12 publications

Automatic shell clustering using a metaheuristic approach

Automatic shell clustering using a metaheuristic approach

Performance Comparison of Invasive Weed Optimization and Particle Swarm Optimization Algorithm for the tuning of Power System Stabilizer in Multi-machine Power System

Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Optimization

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