cultural algorithm

This paper presents a multi-stage model for accurate prediction of demand for dairy products (DDP) by the use of artificial intelligence tools including Multi-Layer Perceptron (MLP), Adaptive-Neural-based Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR). The innovation of this work is the improvement of artificial intelligence tools with various meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Cultural Algorithm (CA). First, the best combination of factors with can affect the DDP is determined by solving a feature selection optimization problem. Then, the artificial intelligent tools are improved with the goal of making a prediction with minimal error. The results indicate that demographic behavior and inflation rate have the greatest impact on dairy consumption in Iran. Moreover, PSO still exhibits a better performance in feature selection in compare of newcomer meta-heuristic algorithms such as IWO and CA. However, IWO shows the best performance in improving the prediction tools by achieving an error of 0.008 and a coefficient of determination of 95%. The final analysis demonstrates the validity and reliability of the results of the proposed model, as it supports the simultaneous analysis and comparison of the outputs of different tools and methods.

This paper is an extension of the well-known vehicle routing problem (VRP) consisting of two stages. The first and second stages deal with the vehicle routing and transportation problems, respectively. Waste collection is one of the applications of the considered problem in a real world situation. A new mathematical model for this type of the problem is presented that minimizes the waste collection cost and decreases the risk posed to the environment for hazardous wastes transportation simultaneously. According to the NP-hard nature of the problem, a new multi-objective hybrid cultural and genetic algorithm (MOHCG) is proposed to obtain Pareto solutions. A straightforward representation for coding the given model is proposed to help us in reducing the computational time. To validate the proposed algorithm, a number of test problems are conducted and the obtained results are compared with the results of the well-known multi-objective evolutionary algorithm, namely non-dominated sorting genetic algorithm (NSGA-II), with respect to some comparison metrics. Finally, the conclusion is provided.

We investigate a one-buyer-multi-vendor co-ordination model with vendor selection problemin a centralized supply chain. In the proposed model, the buyer selects one or more vendorsand orders an appropriate quantity. The quantity discount mechanism is used by all vendors with the aim of coordinating the supply chain. We formulate the problem as a multi objective mixed integer nonlinear mathematical model. Using the Global Criterion method, the proposed model is transformed into a single objective optimization problem. Since, the problem is NP-hard, we propose four meta-heuristic algorithms: Particle Swarm Optimization (PSO), Scatter Search (SS), Population based Harmony Search (HS-pop) and Harmony Search based Cultural Algorithm (HS-CA). The Taguchi’s robust tuning method is applied in order to estimate the optimum values of parameters. Then, the solution quality and computational time of algorithms are compared.