Under mild conditions, it can be induced from the Karush–Kuhn–Tucker condition that the Pareto set, in the decision space, of a continuous Multiobjective Optimization Problems(MOPs) is a piecewise continuous ( 1) m D   manifold(where m is the number of objectives). One hand, the traditional Multiobjective Optimization Algorithms(EMOAs) cannot utilize this regularity property; on the other han...

Co-evolutionary algorithms are evolutionary algorithms in which the given individual’s fitness value estimation is made on the basis of interactions of this individual with other individuals present in the population. In this paper agent-based versions of co-operative co-evolutionary algorithms are presented and evaluated with the use of standard multi-objective test functions. The results of e...

Nowadays, the capability of cloud management suppliers is one of the important advantages for suppliers that can improve the performance and flexibility and reduce costs in companies through easy access to resources. Also, the environmental impacts of suppliers are a significant issue in today’s industrialization and globalization world. This paper analyzes these subjects by fuzzy multi-objecti...

Reducing energy consumption and maintenance costs of a pumping system is seen as an important but difficult multi-objective optimization problem. Many evolutionary algorithms, such particle swarm (PSO), (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) have been used. However, lack comparison between these approaches poses challenge to the selection approach for stormwater drainage ...

in the new production systems, finding a way to improving the product and system reliability in design is a very important. the reliability of the products and systems may improve using different methods. one of this methods is redundancy allocation problem. in this problem by adding redundant component to sub-systems under some constraints, the reliability improved. in this paper we worked on ...

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-obje...

This paper presents a new multi-objective coupled energy and reactive power market clearing model namely the MO-CERPMC model in day-ahead competitive market environment. In proposed model, both the active and reactive power markets are considered as coupled markets and cleared in a same time frame. The multi-objective optimization problem involves the minimization of total payment functions for...

Interpretability and accuracy are two important features of fuzzy systems which are conflicting in their nature. One can be improved at the cost of the other and this situation is identified as “Interpretability-Accuracy Trade-Off”. To deal with this trade-off Multi-Objective Evolutionary Algorithms (MOEA) are frequently applied in the design of fuzzy systems. Several novel MOEA have been propo...

Modern this paper proposes non dominated sorting genetic algorithm (NSGA-II) which has feature of adaptive crowding distance for finding optimal location and sizing of Static Var Compensators (SVC) in order to minimize real power losses and voltage deviation and also to improve voltage profile of a power system at the same time. While finding the optimal location and size of SVC, single line ou...