Here, a quasi-Newton algorithm for constrained multiobjective optimization is proposed. Under suitable assumptions, global convergence of the algorithm is established.

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...

The importance of energy preservation in wireless sensor networks is long established, and a number of maximum lifetime routing schemes exist in the literature. Many of the existing online routing schemes are based on the use of some edge-weight functions, but those schemes exhibit diverse performances in terms of the obtained lifetime results. The major contribution of the present work is to c...

This paper presents a Fuzzy Simulated Evolution algorithm for VLSI standard cell placement with the objective of minimizing power, delay and area. For this hard multiobjective combinatorial optimization problem, no known exact and efficient algorithms exist that guarantee finding a solution of specific or desirable quality. Approximation iterative heuristics such as Simulated Evolution are best...

and fuzzy rules as well as numerical methods was developed for multiobjective optimization of power distribution system operation. The development process and knowledge-acquisition process for the fuzzy knowledge-based system are described in detail. Fuzzy sets are defined for recent temperature trend, line section loading, transformer aging, voltage-level guidelines, and the degree of desirabi...

This paper formulates multiobjective linear programming problems where each coefficient of the objective functions is expressed by a random fuzzy variable. Assuming that the decision maker concerns about the probability that each of the objective function values is smaller than or equal to a certain target value, the fuzzy goals of the decision maker for the probabilities are introduced. Then, ...

This paper discusses the relevance and possible applications of evolutionary algorithms, particularly genetic algorithms, in the domain of knowledge discovery in databases. Knowledge discovery in databases is a process of discovering knowledge along with its validity, novelty, and potentiality. Various genetic-based feature selection algorithms with their pros and cons are discussed in this art...