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This work introduces a hybrid PSO algorithm which includes perturbation operators to keep population diversity. A new neighborhood structure for Particle Swarm Optimization called Singly-Linked Ring is implemented. The approach proposes a neighborhood similar to the ring structure, but which has an innovative neighbors selection. The objective is to avoid the premature convergence into local optimum...
The necessity of approaching the boundary between the feasible and infeasible search space for many constrained optimization problems is a paramount challenge for every constraint-handling technique. It is true that many of the stateof- the-art constraint-handling techniques performs well when facing constrained problems. However, it is a common situation that reaching the boundary between the feasible...
While research on constrained optimization using evolutionary algorithms has been actively pursued, it has had to face the problem that the ability to solve multi-modal problems is insufficient, that the ability to solve problems with equality constraints is inadequate, and that the stability and efficiency of searches is low. We have proposed the εDE, defined by applying the ε constrained method...
Differential Evolution (DE) algorithms belong to Evolutionary Algorithms (EAs). They are widely used for optimizing continuous functions. In this chapter we present a self-adaptive differential evolution algorithm which uses (1) a self-adaptive mechanism on control parameters F and CR, (2) more strategies during the mutation operation, (3) a population size (NP) reduction mechanism during the evolutionary...
In this Chapter we present the modification of a Differential Evolution algorithm to solve constrained optimization problems. The changes include a deterministic and a self-adaptive parameter control in two of the Differential Evolution parameters and also in two parameters related with the constraint-handling mechanism. The proposed approach is extensively tested by using a set of well-known test...
This chapter proposes a constraint handling technique for multi-objective evolutionary algorithms based on an adaptive penalty function and a distance measure. These two functions vary dependent upon the objective function value and the sum of constraint violations of an individual. Through this design, the objective space is modified to account for the performance and constraint violation of each...
Real life optimization problems often involve one or more constraints and in most cases, the optimal solutions to such problems lie on constraint boundaries. The performance of an optimization algorithm is known to be largely dependent on the underlying mechanism of constraint handling. Most population based stochastic optimization methods prefer a feasible solution over an infeasible solution during...
A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired by the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The resulting GA-AIS hybrid is tested in a suite of constrained optimization problems...
An aspect that often causes difficulties when using Genetic Algorithms for optimization is that these algorithms operate as unconstrained search procedures and most of the real-world problems have constraints of different types. There is a lack of efficient constraint handling technique to bias the search in constrained search spaces toward the feasible regions. We propose a novel methodology to be...
Constraint-handling techniques for evolutionary multiobjective aerodynamic and multidisciplinary designs are focused. Because number of evaluations is strictly limited in aerodynamic or multidisciplinary design optimization due to expensive computational fluid dynamics (CFD) simulations for aerodynamic evaluations, very efficient and robust constraint-handling technique is required for aerodynamic...
Artificial Immune Systems (AIS) are computational intelligent systems inspired by some processes or theories observed in the biological immune system. They have been applied to solve a wide range of machine learning and optimization problems. In this chapter the main AIS-based proposals for solving constrained numerical optimization problems are shown. Although the first works were hybrid solutions...
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