Deployment optimization of 3-D sensor networks is essential for the overall cost of the system and the downstream tasks performance. The key of establishing realistic deployment is twofold: a high-fidelity mathematical programming model and an efficient algorithm for solving it. In this paper, we revisit the 3-D sensor networks deployment and present a mixed-variable optimization problem (MVOP) which jointly considers the discrete subset selection decision, continuous orientation decision, and decisionmaking under uncertainty. Based on the proposed real-world application, we innovatively design a mixed-variable correlation-aware genetic algorithm as the solver. Different from mainstream two-partition methods in MVOP, our algorithm captures the problem-specific features of deployment optimization and introduces a correlation-aware search paradigm which interactively updates the discrete and continuous decision variables. On the one hand, we update the discrete part (i.e., subset selection of candidate locations) first and then optimize the continuous part (i.e., sensor orientation parameters). On the other hand, we customize a heuristic mechanism to start with continuous part to identify the suitable discrete part. Experiments demonstrate that our approach can improve the performance of small-scale and large-scale scenarios of deployment by up to 55.7% and 56.4%, respectively, compared to state-of-the-art MVOP algorithms.

An emerging optimisation problem from real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become an active research of the multi-robot system. This paper focuses on a multi-objective MPDA (MO-MPDA) problem which is to design execution plans of robots for minimising the cost of used robots and maximising the efficiency of task execution. The MOMPDA problem has the issues of conflicting objectives, redundant representation, and variable-length encoding, posing extra challenges to address the MO-MPDA problem effectively. Combining the ∊-constraint method and decomposition mechanisms, a novel multi-objective evolutionary algorithm is proposed. The proposed algorithm selects the efficiency objective as the main objective and converts the cost objective as constraints. Thus, the multi-objective problem is decomposed into a series of scalar constrained optimisation subproblems by assigning each subproblem with an upper bound constraint. All the subproblems are optimised and evolved simultaneously with the transferring knowledge from other sub-problems to solve the MO-MPDA problem parallelly and efficiently. Besides, considering the characteristics of parent individuals, this paper designs a hybrid reproduction mechanism to transmit effective information to offspring individuals for tackling the encoding redundancy and varying-length. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of most-used metrics.

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents. This study aims to address the problem of dynamic construction of mission groups under new requirements. Agents are heterogeneous, and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored. In our method, a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions. The degree of matching between the mission requirements and the group’s capabilities, and the communication cost of group formation are used as indicators to evaluate the quality of the group. The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations. The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.