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Bilevel programming has a hierarchical leader-follower structure in which the leader acts as the upper level, and the follower acts as the lower level, respectively. Both of them wish to optimize its objective. In this paper, a heuristic method called teaching-learning based optimization algorithm is proposed for solving nonlinear bilevel programming. The detailed procedure is presented, and the performance...
Adaptation to changes in real scenarios is not a “cost-free” operation. However, in general, this is not considered in most of the studies done in dynamic optimization problems. Our focus here is to analyse what happens when a relocation cost is added in the dynamic maximal covering location problem, that is, when the adaptation to changes entails some cost. Comparing two models with and without “cost-free”...
In this work we introduce a novel approach for bringing collective intelligence methods into the optimization process carried out by evolutionary multi-objective optimization algorithms. Expressing preferences from a unique or small group of decision makers may raise unilateral choices issues and poor hints in terms of search parameter. The extension of the non-dominated sorting genetic algorithm...
This paper presents a non-parameter method to identify the peaks of the multi-modal optimization problems provided that the peaks are characterized by a smaller objective values than their neighbors and by a relatively large distance from points with smaller objective value. Using the identified peaks as the seeds, we decompose the population into some subpopulations and dynamically allocate the computational...
In the traditional improved Particle Swarm Optimization algorithms, the search spaces of the particles are always fixed. In this paper, based on the standard particle swarm optimization (PSO) algorithm, a dynamic search space particle swarm optimization algorithm (DSPPSO) based on population entropy is proposed. The population entropy is introduced to describe the particles' location confusion degree,...
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