4,387 research outputs found
A new hybrid evolutionary algorithm for the treatment of equality constrained MOPs
Multi-objective evolutionary algorithms are widely used by researchers and practitioners to solve multi-objective optimization problems (MOPs), since they require minimal assumptions and are capable of computing a finite size approximation of the entire solution set in one run of the algorithm. So far, however, the adequate treatment of equality constraints has played a minor role. Equality constraints are particular since they typically reduce the dimension of the search space, which causes problems for stochastic search algorithms such as evolutionary strategies. In this paper, we show that multi-objective evolutionary algorithms hybridized with continuation-like techniques lead to fast and reliable numerical solvers. For this, we first propose three new problems with different characteristics that are indeed hard to solve by evolutionary algorithms. Next, we develop a variant of NSGA-II with a continuation method. We present numerical results on several equality-constrained MOPs to show that the resulting method is highly competitive to state-of-the-art evolutionary algorithms.Peer ReviewedPostprint (published version
An evolutionary algorithm based pattern search approach for constrained optimization
Constrained optimization is one of the popular
research areas since constraints are usually present in most real
world optimization problems. The purpose of this work is to
develop a gradient free constrained global optimization methodology
to solve this type of problems. In the methodology proposed,
the single objective constrained optimization problem is solved
using a Multi-Objective Evolutionary Algorithm (MOEA) by
considering two objectives simultaneously, the original objective
function and a measure of constraint violation. The MOEA
incorporates a penalty function where the penalty parameter
is estimated adaptively. The use of penalty function method
will enable to further improve the current best solution by
decreasing the level of constraint violation, which is made using
a gradient free local search method. The performance of the
proposed methodology was assessed on a set of benchmark
test problems. The results obtained allowed to conclude that
the present approach is competitive when compared with other
methods available
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
Use of genetic algorithms and gradient based optimization techniques for calcium phosphate precipitation
Phase equilibrium computations constitute an important problem for designing and optimizing crystallization processes. The Gibbs free
energy is generally used as an objective function to find phase amount and composition at equilibrium. In such problems, the Gibbs free
energy may be a quite complex function, with several local minima. This paper presents a contribution to handle this kind of problems by
implementation of an optimization technique based on the successive use of a genetic algorithm (GA) and of a classical sequential quadratic
programming (SQP) method: the GA is used to perform a preliminary search in the solution space for locating the neighborhood of the
solution. Then, the SQP method is employed to refine the best solution provided by the GA. The basic operations involved in the design of
the GA developed in this study (encoding with binary representation of real values, evaluation function, adaptive plan) are presented. Several
test problems are first presented to demonstrate the validity of the approach. Then, calcium phosphate precipitation which is of major interest
for P-recovery from wastewater, has been chosen as an illustration of the implemented algorithm
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