This paper presents a scheme for generating the Pareto front of multiobjective optimization problems by solving a sequence of constrained single-objective problems. Since the necessity of determining the constraint value a priori can be a serious drawback of the original epsilon-constraint method, our scheme generates appropriate constraint values adaptively during the run. A simple example pro...

Pareto archived dynamically dimensioned search (PA-DDS) has been modified to solve combinatorial multi-objective optimization problems. This new PA-DDS algorithm uses discrete-DDS as a search engine and archives all non-dominated solutions during the search. PA-DDS is also hybridized by a general discrete local search strategy to improve its performance near the end of the search. PA-DDS inheri...

In our previous work [1], it has been shown that the performance of multi-objective evolutionary algorithms can be greatly enhanced if the regularity in the distribution of Pareto-optimal solutions is used. This paper suggests a new hybrid multi-objective evolutionary algorithm by introducing a convergence based criterion to determine when the modelbased method and when the genetics-based metho...

In recent years, the research focus in multi-objective optimization has shifted from approximating the Pareto optimal front in its entirety to identifying solutions that are well-balanced among their objectives. Proper Pareto optimality is an established concept for eliminating Pareto optimal solutions that exhibit unbounded tradeoffs. Imposing a strict tradeoff bound allows specifying how many...

Feature selection for transient classification is the problem of choosing among several monitored parameters (i.e., the features) those to be used for efficiently recognizing the developing transient patterns. It is a critical issue for the application of “on condition” diagnostic techniques in complex systems, like the nuclear power plants, where hundreds of parameters are measured. Indeed, ir...

Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, widely used approach is based on the Pareto front. In this document, proposal made analysis of optimal front problems using clustering techniques. With approach, an sought further use and improvement considering solutions clusters found. To carry out clustering, metho...

The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the coevolutionary aspect of the IPCA algorithm is utilized to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. Empirical results indicate that such a scheme sig...