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...

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, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. Two objective functions, real power loss and energy not supplied index (ENS), were utilized. Multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...

In multi-objective optimization the objective is to reach a point which is Pareto efficient. However we usually encounter many such points and choosing a point amongst them possesses another problem. In many applications we are required to choose a point having a good spread over all objective functions which is a direct consequence of the notion of fairness. In this paper we propose two things...

In this paper, we show how cellular structures can be combined with a multi-objective genetic algorithm (MOGA) for improving its search ability to find Pareto-optimal solutions of multi-objective optimization problems. We propose an assignment method of a different search direction to each cell for implementing a cellular MOGA. In our cellular MOGA, every individual in each population exists in...

In this paper, based on Magnetotactic Bacteria Optimization Algorithm(MBOA), magnetotactic bacterium multi-objective optimization algorithm (MBMOA) is proposed for solving multi-objective optimization problems(MOPs). Magnetotactic bacterium optimization algorithm is a novel random research algorithm which simulates the process of magnetotactic bacteria (MTB) producing magnetosomes(MTS) to regul...

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test fun...

compared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points of a water distribution system (wds). however, control of trihalomethane (thm) formation as a potentially carcinogenic disinfection by-product ...

In this paper proposes a fuzzy multi-objective hybrid Genetic and Bee colony optimization algorithm(GA-BCO) to find the optimal restoration of loads of power distribution network under fault.Restoration of distribution systems is a complex combinatorial optimization problem that should beefficiently restored in reasonable time. To improve the efficiency of restoration and facilitate theactivity...