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We address the problem of multiple local optima commonly arising inoptimization problems for multi-agent systems, where objective functions arenonlinear and nonconvex. For the class of coverage control problems, we proposea systematic approach for escaping a local optimum, rather than randomlyperturbing controllable variables away from it. We show that the objectivefunction for these problems can be decomposed to facilitate the evaluation ofthe local partial derivative of each node in the system and to provide insightsinto its structure. This structure is exploited by defining "boostingfunctions" applied to the aforementioned local partial derivative at anequilibrium point where its value is zero so as to transform it in a way thatinduces nodes to explore poorly covered areas of the mission space until a newequilibrium point is reached. The proposed boosting process ensures that, atits conclusion, the objective function is no worse than its pre-boosting value.However, the global optima cannot be guaranteed. We define three families ofboosting functions with different properties and provide simulation resultsillustrating how this approach improves the solutions obtained for this classof distributed optimization problems.

Escaping Local Optima in a Class of Multi-Agent Distributed Optimization Problems: A Boosting Function Approach