| 1. |
- Frasheri, M., et al.
(författare)
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GLocal : A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
- 2023
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Ingår i: IEEE Symposium Series on Computational Intelligence, SSCI. - : IEEE. - 9781665430654 ; , s. 1696-1703
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Konferensbidrag (refereegranskat)abstract
- Multi-agent systems can be prone to failures during the execution of a mission, depending on different circumstances, such as the harshness of the environment they are deployed in. As a result, initially devised plans for completing a mission may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. There are two main approaches to solve the re-planning problem (i) global re-planning techniques using a centralized planner that will redo the task allocation with the updated world state and (ii) decentralized approaches that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots, better suited to a dynamic environment and less computationally expensive. In this paper, we propose a hybrid approach, named GLocal, that combines both strategies to exploit the benefits of both, while limiting their respective drawbacks. GLocal was compared to a planner-only, and an agent-only approach, under different conditions. We show that GLocal produces shorter mission make-spans as the number of tasks and failed agents increases, while also balancing the tradeoff between the number of messages exchanged and the number of requests to the planner.
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| 2. |
- Ramos-Michel, A., et al.
(författare)
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Improving Metaheuristic Algorithm Design Through Inequality and Diversity Analysis : A Novel Multi-Population Differential Evolution
- 2023
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Ingår i: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). - : IEEE. - 9781665430654 ; , s. 1547-1552
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Konferensbidrag (refereegranskat)abstract
- In evolutionary algorithms and metaheuristics, defining when applying a specific operator is important. Besides, in complex optimization problems, multiple populations can be used to explore the search space simultaneously. However, one of the main problems is extracting information from the populations and using it to evolve the solutions. This article presents the inequality-based multi-population differential evo-lution (IMDE). This algorithm uses the K-means to generate subpopulations (settlements). Two variables are extracted from the settlements, the diversity and the Gini index, which measure the solutions' distribution and the solutions' inequality regarding fitness. The Gini index and the diversity are used in the IMDE to dynamically modify the scalation factor and the crossover rate. Experiments over a set of benchmark functions with different degrees of complexity validate the performance of the IMDE. Besides comparisons, statistical and ranking average validate the search capabilities of the IMDE.
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