scheduling for job shop is very important in both fields of production management and combinatorial op-timization. however, it is quite difficult to achieve an optimal solution to this problem with traditional opti-mization approaches owing to the high computational complexity. the combination of several optimization criteria induces additional complexity and new problems. in this paper, we pro...

this paper focuses on multi-objective designing of multi-machine thyristor controlled series compensator (tcsc) using strength pareto evolutionary algorithm (spea). the tcsc parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a spea ...

Resource allocation is the optimal distribution in a limited number of resources available for certain activities. The large activities requires exponentially multiplying computation cost. Therefore, resource problem known as NP-Hard literature. In this study, multi-objective binary artificial bee colony algorithm has been proposed solving problems. benefited from robust structure and easy impl...

Abstract In many real-world situations, we have to deal with multiple objectives simultaneously in order make appropriate decisions. The presence of an optimization problem makes the challenging because most time these are conflicting nature. For example, may want maximize return on investment a portfolio and, other hand, minimize risk associated assets portfolio. We cost product while maximizi...

In general, Multi-objective Evolutionary Algorithms do not guarantee find solutions in the Pareto-optimal set. We propose a new approach for solving decomposable deceptive multi-objective problems that can find all solutions of the Pareto-optimal set. Basically, the proposed approach starts by decomposing the problem into subproblems and, then, combining the found solutions. The resultant appro...

Hydrologic models simulate the river flow from the contributing basin for a given river. For the simulation process, the integration domain is discretized into computational cells. The inputs for such models are precipitation ratio and the initial flow. There are many parameters to be determined for an operational model, including the type of soil (porosity field, water flux between the bottom ...