The aim of this work is to provide a comprehensive review of multiobjective optimization in the image segmentation problem based on image thresholding. The authors show that the inclusion of several criteria in the thresholding segmentation process helps to overcome the weaknesses of these criteria when used separately. In this context, they give a recent literature review, and present a new multi-level image thresholding technique, called Automatic Threshold, based on Multiobjective Optimization (ATMO). That combines the flexibility of multiobjective fitness functions with the power of a Binary Particle Swarm Optimization algorithm (BPSO), for searching the “optimum” number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare with this segmentation method, based on the multiobjective optimization approach with Otsu’s, Kapur’s, and Kittler’s methods. Experimental results show that the thresholding method based on multiobjective optimization is more efficient than the classical Otsu’s, Kapur’s, and Kittler’s methods.

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In this work, we propose a new approach for coordinating generated agents’ plans dynamically. The purpose is to take into consideration new conflicts introduced in new versions of agents’ plans. The approach consists in finding the best combination which contains one plan for each agent among its set of possible plans whose execution does not entail any conflict. This combination of plans is reconstructed dynamically, each time agents decide to change their plans to take into account unpredictable changes in the environment. This not only ensures that new conflicts are likely to be introduced in the new plans that are taken into account but also it allows agents to deal, solely, with the execution of their actions and not with the resolution of conflicts. For this, we use genetic algorithms where the proposed fitness function is defined based on the number of conflicts that agents can experience in each combination of plans. As part of our work, we used a concrete case to illustrate and show the usefulness of our approach.

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We propose, in this paper, a new holonic agent testing technique. The technique is based on models and uses genetic algorithms. It considers the successive versions of an agent. The approach is organized in two main phases that are conducted iteratively. The first phase is concerned with detecting a new version of an agent under test. The second phase focuses on testing each new detected version. The new version is analyzed in order to generate a behavioral model on which is based the generation of test cases. The test case generation process focuses on the new parts of the agent behavior. In this way, the technique supports an incremental update of test cases.