Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall ...

In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the f...

In this paper, we describe a multi-objective evolutionary algorithm, that uses clustering selection and does not need any additional parameter like others. It clusters the population into a exible number of clusters employing x-means from [Pelleg and Moore, 2000]. First, the selective tness is assigned to clusters and in second place to individuals of clusters. We show three hybrid variants inc...

Evolutionary Algorithms are recognized to be efficient to deal withMulti-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a newMulti-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted SumMethod (WSM) and Dynamical Evolutionary Algorithm (DEA) is proposed. Some fa...

We consider a possibility of automating the analysis of a computer program realizing the objective function of an extremal problem, and of distributing the calculation of the function value into parallel processes on the basis of results of the analysis. The first problem is to recognize the constituent parts of the function. The next one is to determine their computing times. The third problem...

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default pa...

Multiobjective optimization is progressively more applied as it allows being closer to real engineering problems that may be found in industrial applications. This paper proposes applied techniques to optimize the design of a pressure vessel for hollow cylinder and hemi-spherical head in terms of the cost of the material and manufacturing. The problem is studied with two objectives: minimise co...

A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of...