A method to enhance the 'possibilistic C-means with repulsion' algorithm based on cluster validity index p. 77 Design centering and tolerancing with utilization of evolutionary techniques p. 91 Curve fitting with NURBS using simulated annealing p. 99 Multiobjective adaptive representation evolutionary algorithm (MAREA)-a new evolutionary algorithm for multiobjective optimization p. 113 Adapting...

The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: the multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that t...

In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses evolutionary programming, Pareto ranking and elitism (i.e., an external population). The approach proposed is validated using several examples taken from the specialized literature. Our results are compared with respect to the NSGA-II, which is an algorithm r...

An important approach for unsupervised landcover classification in remote sensing images is the clustering of pixels in the spectral domain into several partitions. In this paper, a multiobjective optimization algorithm is utilized to tackle the problem of partitioning where a number of different cluster validity indices are simultaneously optimized. New multiobjective clustering algorithm uses...

Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Paretobased ranking and crowding distance sorting, used by state-of...

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly a...

Fuzzy optimization deals with the problem of determining ’optimal’ solutions of an optimization problem when some of the elements that appear in the problem are not precise. In real situations it is usual to have information, in systems under consideration, that is not exact. This imprecision can be modeled in a fuzzy environment. Zadeh [20] analyzed systems of logic that permit truth values be...

Fuzzy optimization deals with the problem of determining ’optimal’ solutions of an optimization problem when some of the elements that appear in the problem are not precise. In real situations it is usual to have information, in systems under consideration, that is not exact. This imprecision can be modeled in a fuzzy environment. Zadeh [20] analyzed systems of logic that permit truth values be...

A novel tuning method is proposed for the design of fuzzy PID controllers for multivariable systems. In the proposed method, a PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and ...