In this paper, lift and drag coefficients were numerically investigated using NUMECA software in a set of 4-digit NACA airfoils. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks were then obtained for modeling both lift coefficient (CL) and drag coefficient (CD) with respect to the geometrical design parameters. After using such obtained polynomial n...

offshore jacket-type towers are steel structures designed and constructed in marine environments for various purposes such as oil exploration and exploitation units, oceanographic research, and undersea testing. in this paper a newly developed meta-heuristic algorithm, namely cyclical parthenogenesis algorithm (cpa), is utilized for sizing optimization of a jacket-type offshore structure. the a...

In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan);the second objective function is to minimize the cost of using renewable resources. Non-renewable resources are also considered as parameters in this model. The preemption of activities ...

the markowitz’s optimization problem is considered as a standard quadratic programming problem that has exact mathematical solutions. considering real world limits and conditions, the portfolio optimization problem is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. therefore, the use of meta-heuristic methods such as neural networks and evolutionar...

In this paper, two-dimensional cutting stock problem with demand has been studied.In this problem, cutting of large rectangular sheets into specific small pieces should be carried out hence, the waste will be minimized. Solving this problem is important to decrease waste materials in any industry that requires cutting of sheets. In most previus studies, the demand of pieces has not been usually...

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

Subspace clustering is one of the efficient techniques for determining clusters in different subsets dimensions. Ideally, these should find all possible non-redundant which data point participates. Unfortunately, existing hard subspace algorithms fail to satisfy this property. Additionally, with increase dimensions data, classical become inefficient. This work presents a new density-based algor...

Meta-heuristic techniques are important as they used to find solutions computationally intractable problems. Simplistic methods such exhaustive search become expensive and unreliable the solution space for algorithms increase. As no method is guaranteed perform better than all others in classes of optimization problems, there a need constantly new and/or adapt old algorithms. This research prop...

This work presents three multi-objective heuristic algorithms based on Two-phase Pareto Local Search with VNS (2PPLS-VNS), Multi-objective Variable Neighborhood Search (MOVNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The algorithms were applied to the open-pit-mining operational planning problem with dynamic truck allocation (OPMOP). Approximations to Pareto sets generated by t...