Abstract
Evolutionary multi-agent systems (EMAS) are very good at dealing with difficult, multi-dimensional problems. Currently, research is underway to improve this algorithm, giving even more freedom to agents not only in solving the problem but also in making decisions on the behavior of the algorithm. One way is to hybridize this algorithm with other existing algorithms creating Hybrid Evolutionary Multi Agent-System (HEMAS). Unfortunately, such connections generate problems in the form of an unbalanced energy level of agents who have made the decision to use such an improvement. One of the solutions is the mechanism of redistributing the agents' energy in the form of an operator. The article presents several proposals of redistribution operators along with numerous experimental results.
Highlights
Despite the increase in computer performance, not all problems can be resolved in a timely manner
Some problems solved by deterministic algorithms take too long or are too complicated
We are still looking for new metaheuristics because it is impossible to find a single method that will solve all problems with the same accuracy
Summary
Despite the increase in computer performance, not all problems can be resolved in a timely manner. Some problems solved by deterministic algorithms take too long or are too complicated (adding dozens of cities to the TSP problem is such an example). Some problems do not have deterministic solutions. In these and other cases, novel stochastic methods can help. If other solutions do not meet the assumed goals, you can use metaheuristics. Their big advantage is that they do not require information about the characteristics of the search space. We are still looking for new metaheuristics because it is impossible to find a single method that will solve all problems with the same accuracy (cf Wolpert and MacReady [19])
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