Exact optimization algorithms are not able to provide an appropriate solution in solving optimization problems with a high-dimensional search space. In these problems, the search space grows exponentially with the problem size therefore; exhaustive search is not practical. Also, classical approximate optimization methods like greedy-based algorithms make several assumptions to solve the problem...

in today's globalization, an effective integration of production and distribution plans into a unified framework is crucial for attaining competitive advantage. this paper addresses an integrated multi-product and multi-time period production/distribution planning problem for a two-echelon supply chain subject to the real-world variables and constraints. it is assumed that all transportations a...

in this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. since cell formation problem is np-hard, two algorithms based on genetic and modified particle swarm optimization (mpso) algorithms are developed to solve the problem. for generating initial solutions in these algorithms, a new heuristic method is developed, which always cre...

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...

according to the current state of water resources and the increasing demand for water, the supply of future needs will be faced with serious constraints. on the one hand, the failure to supply sometimes causes social and political problems. on the other hand, improper exploitation of water resources will lead to irreparable damages. therefore, the use of optimization models for the planning of ...

This paper presents a new mathematical model to solve cell formation problem in cellular manufacturing systems, where inter-arrival time, processing time, and machine breakdown time are probabilistic. The objective function maximizes the number of operations of each part with more arrival rate within one cell. Because a queue behind each machine; queuing theory is used to formulate the model. T...

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 ...

Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. In this case, global optimization techniques are required in order to avoid the local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently ...

gravitational search algorithm (gsa) is one of the newest swarm based optimization algorithms, which has been inspired by the newtonian laws of gravity and motion. gsa has empirically shown to be an efficient and robust stochastic search algorithm. since introducing gsa a convergence analysis of this algorithm has not yet been developed. this paper introduces the first attempt to a formal conve...