A novel numerical optimization algorithm inspired from weed colonization

By A. Mehrabian, C. Lucas. Ecological Informatics, 2006

1,415 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/734c66e3757620d3d4016410057ee92f72a9853d

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Citations in the corpus

A new bio-inspired optimisation algorithm: Bird Swarm Algorithm (2016, methodology): “Inspired by the plant, Invasive Weed Optimization (Mehrabian & Lucas, 2006) and Flower Pollination Algorithm (Yang, Karamanoglu, & He, 2014) were designed.”
A Brief Review of Nature-Inspired Algorithms for Optimization (2013, background): “ez [12] Group search optimizer He et al. [26] Cat swarm Chu et al. [7] Human-Inspired Algorithm Zhang et al. [80] Consultant-guided search Iordache [29] Invasive weed optimization Mehrabian and Lucas [42] Cuckoo search Yang and Deb [74] Marriage in honey bees Abbass [1] Eagle strategy Yang and Deb [”
A Survey of Bio inspired Optimization Algorithms (2012, background): “Invasive Weed Optimization (IWO) is a numerical stochastic search algorithm proposed by Mehrabian and Lucas in 2006 [19], inspired by the ecological process of weed colonization and distribution.”
Water wave optimization: A new nature-inspired metaheuristic (2015, methodology): “IWO and BBO respectively rank first and second on f9, followed by WWOwhich is significantly different from (better than) GSA, HuS and BA. f9, the shifted and rotated Rastrigin's function, has a huge Table 2 Comparative results on unimodal benchmark functions.”
Optimization Criteria for Optimal Placement of Piezoelectric Sensors and Actuators on a Smart Structure: A Technical Review (2010, methodology): “…1984), simulated annealing (Kirkpatrick et al., 1983; Cerny, 1985), tabu search (Glover and Laguna, 1997), genetic algorithms (Goldberg, 1989; Mitchell, 1996), sensitivity analysis (Conte et al., 2003; Saltelli et al., 2004), gradient algorithm (Haftka and Gurdal, 1993), invasive weed…”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “Invasive Weed Optimization (IWO) The Invasive Weed Optimization Algorithm (IWO) was proposed by Mehrabian & Lucas (2006) [80], as an ecologically inspired metaheuristic that mimics the process of weeds colonization and distribution, which is capable of solving multidimensional, linear and nonlinear ”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “He and Hui [215], DE – Das et al. [2], PSO – Sun et al. [225], Du et al. [229], Thangavel et al. [236], AIS – Lie et al. [260], Review – Jiang et al. [30], Lukashin et al. [31], Xu and Wunsch [91], Hruschka et al. [3,119,120], Jain et al. [34], MODE – Suresh et al. [343] Intrusion detection GA – Liu”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “2006 Invasive Weed Optimization (IWO) Mehrabian and Lucas (2006) 22.”
Invasive Weed Optimization and its Features in Electromagnetics (2010, background): “It was shown that the value of nonlinear modulation index has a considerable effect on the performance of IWO [14].”
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, background): “Some of the advantages of IWO include the manner of reproduction, spatial dispersal, and competitive exclusion (Mehrabian and Lucas, 2006) as well as the fact that seeds and their parents are ranked together and that those with better fitness survive and become reproductive (Ahmed et”