A Multiagent-Based Framework for Self-Adaptive Software with Search-Based Optimization

Planning a suitable solution to adapt to software changes is the most important and fundamental ability of self-adaptive software (SAS). However, with the increasing complexities of managed resources, context and user preferences, existing self-adaptive planning approaches need to be improved to deal with the complex changes which are multiple, interrelated and evolving. Search-based optimization (SBO) is well-suited to deal with multiple and complex problems. Hence, using SBO as a new self-adaptive planning approach may be a particularly promising research trajectory. This paper proposes a multi-agent framework for SAS with SBO to deal with complex changes, reduce maintenance time and cost, and enhance software quality. This framework defines a special software architecture of SAS to choose different planning approaches, uses the SBO to plan solutions for complex changes, and supports the online planning by multi agents. In addition, a corresponding workbench is being established to develop SAS according to this framework.

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