Divide-and-Conquer Evolutionary Multitasking Optimization
提出一种分而治之的进化多任务优化方法，将复杂问题分解为多个简单子任务并行优化，并设计自适应策略分配资源，在高光谱解混中验证了有效性。
This article proposes a novel evolutionary multitasking optimization (EMTO) paradigm called divide-and-conquer EMTO, which divides the original complex optimization problem into multiple simple optimization tasks and then these tasks are optimized by EMTO concurrently to formulate the resulting solution of the original problem. The main characteristics of divide-and-conquer EMTO are that the considered problem can be divided into multiple small-scale optimization tasks and the optimal solution is the combination of solutions of all tasks. In order to achieve the optimal combined fitness of all tasks, a relative improvement function and an adaptive exploration optimization strategy are designed for dynamic resource allocation across tasks. Finally, a case study on hyperspectral unmixing is investigated in the proposed divide-and-conquer EMTO framework by dividing the hyperspectral image into several homogeneous regions to formulate multiple sparse unmixing tasks. Experiments on benchmark and sparse unmixing problems demonstrate the superiority of divide-and-conquer EMTO.