NAAM-MOEA/D-Based Multitarget Firepower Resource Allocation Optimization in Edge Computing

In the edge environment, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been widely used in the research of multitarget firepower resource allocation. However, as the MOEA/D algorithm uses a fixed neighborhood update mechanism, it is impossible to rationally allocate computing resources based on the difficulty of each subproblem optimization, which results in some problems such as reduced population evolution efficiency and poor evolution quality during the calculation process. In order to solve these problems, a decision mechanism for subproblems and population evolution stages is designed, and on this basis, a MOEA/D algorithm based on the neighborhood adaptive adjustment mechanism is proposed to adapt to the edge environment. The optimization model of multiobjective firepower resource allocation based on the maximization of damage effect and the minimization of strike cost is constructed and solved. Using the ZDT series of test functions for comparative experiments, the simulation results show that the proposed algorithm can balance the distribution and convergence of population evolution and obtain satisfactory optimization results.

Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

Decomposing the large-scale problem into small-scale subproblems and optimizing them cooperatively are critical steps for solving large-scale optimization problem. This article proposes a cooperative differential evolution with utility-based adaptive grouping. The problem decomposition is adaptively executed by the two mechanisms of circular sliding controller and relation matrix, which consider the variable interactions on the basis of the short-term and long-term utilities, respectively. The circular sliding controller provides baselines for the subproblem optimizer. The size of the sliding window and the sliding speed in the controller are adjusted adaptively so that the variables with higher activeness can be optimized extensively. The relation matrix–based grouping strategy enables interacted variables to be grouped into the same subproblem with higher probabilities. The novelty is that decomposition is conducted as the optimization process without extra computational burden. For subproblem optimization, we use a self-adaptive differential evolution operator that adaptively adjusts the parameters to guide the search to the optimum solutions of the subproblems. Experiments on the benchmarks of CEC2008 and CEC2010, and practical problems show the effectiveness of the proposed algorithm.