A new powerful optimization algorithm inspired from colonizing weeds is utilized to solve the well-known quadratic assignment problem (QAP) which is of application in a large number of practical areas such as plant layout, machinery layout and so on. A set of reference numerical problems from QAPLIB is taken in order to evaluate the efficiency of the algorithm compared with the previous ones which had been applied to solve the addressed problem. The results indicate that the algorithm outperforms the competitive ones for a sizable number of the problems as the problems’ dimensions increase.

Most of the methods proposed for segmenting image objects are the supervised methods which are costly due to their requirement for large amounts of labeled data. However, in this article, we present a method for segmenting objects based on a meta-heuristic optimization that does not require any training data. This procedure consists of the two main stages of edge detection and texture analysis. In the edge detection stage, we utilize invasive weed optimization and local thresholding. The edge detection methods that are based on the local histograms are efficient methods but it is very difficult to determine the desired parameters manually. In addition, these parameters must be selected specifically for each image. In this paper, a method is presented for the automatic determination of these parameters using an evolutionary algorithm. The evaluation of this method demonstrates its high performance on natural images.

One of the most important issues in water resources management, is the optimal operation of dam reservoirs. In this research metaheuristic invasive weed optimization algorithm was used to find optimal water allocation strategies for a period of 10 years in Tazeran dam that located in the west of Iran. The developed model in this research is provided for optimal operation in the present state of cropping pattern. The downstream field of Tazeran dam are divided into two zones consist of Tazeran and Evandeh. The defined objective function is to minimize total deficiencies during the simulation period. In order to investigate the reservoir performance indicators of time reliability, percentage to provide volume and vulnerability is used. The results of IWO algorithm also compared with the results of the Genetic algorithm method. Due to the inadequate and inappropriate distribution of Tazeran river flow, simulations have been done in two states, use of surface water flows alone and the combination of surface and underground water flow. Result shows that using IWO and GA method and use of surface and ground water simultaneously, Tazeran dam is able to provide 77. 2% and 76. 24% of the total downstream of water requirement. Also, reliability and vulnerability indicators in Tazeran region are 55. 45% and 33%, respectively, in the IWO method and 52. 72% and 41% in the GA method.

In this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The K-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhouette index. The efficiency of each cluster of stations was determined using the Charnes, Cooper and Rhodes input-oriented data envelopment analysis model. A bi-objective zero one programming model was used to select a Pareto optimal combination of rank and distance of stations. The Pareto solutions for the presented biobjective model were determined using the invasive weed optimization method. Although the proposed methodology is meant for the selection of repair and maintenance stations in an oil refinery Company, it can be used in multi-criteria decision-making problems.

In previous investigations in the eld of exible ow shop scheduling problems, the rework probability for operations was ignored. As these kinds of problems are NPhard, we present an Enhanced invasive weed optimization (EIWO) algorithm in order to solve the addressed problem with probable rework times, transportation times with a conveyor between two subsequent stages, dierent ready times and anticipatory sequence dependent setup times. The optimization criterion is to minimize makespan. Although invasive weed optimization (IWO) is an ecient meta-heuristic algorithm and has been used by many researchers recently, to increase the capability of IWO, we added a mutation operation to enhance the exploration in order to prevent sticking in local optimum. In addition, an anity function is embedded to obstruct premature convergence. With these changes, we balance the exploration and exploitation of IWO. Since the performance of our proposed algorithm depends on parameters values, we apply the popular design of an experimental methodology, called the Response Surface Method (RSM). To evaluate the proposed algorithm, rst, some random test problems are generated and compared with three benchmark algorithms. The related results are analyzed by statistical tools. The experimental results and statistical analyses demonstrate that the proposed EIWO is eective for the problem.

With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of invasive weed optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, forming feature vectors, and final classification. In the presented model, the authors formed a feature vector for each document by means of weighting features use for IWO. Then, documents are trained with NB classifier; then using the test, similar documents are classified together. FS do increase accuracy and decrease the calculation time. IWO-NB was performed on the datasets Reuters-21578, WebKb, and Cade 12. In order to demonstrate the superiority of the proposed model in the FS, Genetic Algorithm (GA) and Particle Swarm optimization (PSO) have been used as comparison models. Results show that in FS the proposed model has a higher accuracy than NB and other models. In addition, comparing the proposed model with and without FS suggests that error rate has decreased.

In the last decade, various methods are created to optimize time, cost, and quality. Solving such a problem on large scale is too hard using traditional methods in logical time. Recently, researchers are focused on a meta-heuristic algorithm to solve time-cost-quality tradeoff problems. How to make a balance among time, cost, and quality parameters is so critical in construction project management. In this study, an invasive weed optimization algorithm is applied to solve the problem. In the proposed model, activity time is changed so that maximum usage of resources is obtained. In other words, it is possible to perform some activity simultaneously if their duration is increased which causes to decrease time, cost and increase project quality. Obtained results indicate the advantages of the proposed algorithm. Finally, to validate the proposed model a small size instance problem is created and solved by GAMS software optimally and compared with proposed algorithm results in MATLAB software. Results show that both Pareto solution obtained is almost identical, then it validates the algorithms for large scale problem.