Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdi...

( Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled w...

Evolutionary algorithms (EAs) such as evolution strategies and genetic algorithms have become the method of choice for optimization problems that are too complex to be solved using deterministic techniques such as linear programming or gradient (Jacobian) methods. The large number of applications (Beasley (1997)) and the continuously growing interest in this field are due to several advantages ...

this paper presents a biogeography-based optimization (bbo) algorithm to solve the economic loaddispatch (eld) problem with generator constraints in thermal plants. the applied method can solvethe eld problem with constraints like transmission losses, ramp rate limits, and prohibited operatingzones. biogeography is the science of the geographical distribution of biological species. the modelsof...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographic analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem to be used on others. The purpose of this paper is to describe a framework that unifies the design and implem...

in recent year, the evolutionary structural optimization (eso) method, has been applied as an effective tool for structures design. the concept of eso method is to use the finite element  results in each iteration of process and removal of inefficient material based on these results. in this method there are several experimental parameters such as rejection rate and evolutionary rate that are e...

Providing stock buying with more profit for buyer is challenging procedure in operation. In other words, buyers expect less cost. Many influence parameters cause this being attractive. One of them liquidity. There are some measurements With complete and detailed model variables the static environment (with no changing conditions), we can use common approach optimization to buy proper stocks, bu...