The De-Novo programming problem proposed by Zeleny is well-known for its value on designing an optimal system by extending existed resources instead of finding the optimum in a given system with fixed resources. Since few papers are dedicated to explore the De-Novo programming problem with multiple stages and its resolution approach, the De-Novo programming problem is innovatively extended to a...

Biogeography-Based Optimization Algorithm (BBOA) is a kind of new global optimization algorithm inspired by biogeography. It mimics the migration behavior of animals in nature to solve optimization and engineering problems. In this paper, BBOA for the Set Covering Problem (SCP) is proposed. SCP is a classic combinatorial problem from NP-hard list problems. It consist to find a set of solutions ...

Motivation Most problems in nature have several (possibly conflicting) objectives to be satisfied. Many of these problems are frequently treated as single-objective optimization problems by transforming all but one objective into constraints. What is a multiobjective optimization problem? The Multiobjective Optimization Problem (MOP) (also called multicriteria optimization, multiperformance or ...

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...

Learning graphical model parameters from incomplete data is a non-convex optimization problem. Iterative algorithms, such as Expectation Maximization (EM), can be used to get a local optimum solution. However, little is known about the quality of the learned local optimum, compared to the unknown global optimum. We exploit variables that are always observed in the dataset to get an upper bound ...

Planning for supply water demands (drinkable and irrigation water demands) is a necessary problem. For this purpose, three subjects must be considered (optimization of water supply systems such as volume of reservoir dams, optimization of released water from reservoir and prediction of next droughts). For optimization of volume of reservoir dams, yield model is applied. Reliability of yield mod...

Local search techniques have been applied in variant global optimization methods. The eeect of local search to the function landscape can make multimodal problems easier to solve. For evolutionary algorithms, the usage of the step size control concept normally will result in failure by the individual to escape from the local optima during the nal stage. In this paper, we propose an algorithm co...

In this paper, we propose a new population-based framework for combining local search with global explorations to solve single-objective unconstraint numerical optimization problems. The idea is to use knowledge about local optima found during the search to a) locate promising regions in the search space and b) identify the suitable step size to move from one optimum to others in each region. T...