Reconfigurable Intelligent Surface (RIS) is a revolutionizing approach to provide cost-effective yet energy-efficient communications. The transmit beamforming of the base station (BS) and discrete phase shifts of the RIS are jointly optimized to provide high quality of service. However, existing works ignore the high dependence between the large number of phase shifts and estimate them separately, consequently, easily getting trapped into local optima. To investigate the number and distribution of local optima, we conduct a fitness landscape analysis on the sum rate maximization problems. Two landscape features, the fitness distribution correlation and autocorrelation, are employed to investigate the ruggedness of landscape. The investigation results indicate that the landscape exhibits a rugged, multi-modal structure, i.e., has many local peaks, particularly in the cases with large-scale RISs. To handle the multi-modal landscape structure, we propose a novel niching genetic algorithm to solve the sum rate maximization problem. Particularly, a niching technique, nearest-better clustering, is incorporated to partition the population into several neighborhood species, thereby locating multiple local optima and enhance the global search ability. We also present a minimum species size to further improve the convergence speed. Simulation results demonstrate that our method achieves significant capacity gains compared to existing algorithms, particularly in the cases with large-scale RISs.
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization
Huazhong University of Science and Technology（华中科技大学）
大多数现实世界中的优化问题往往会出现多个全局最优或局部最优。因此，越来越多的小生境元启发式算法，致力于寻找多个最优在一个单一的运行，被开发来解决这些多峰优化问题。然而，现有的小生境元启发式算法存在两个亟待解决的难题：如何针对不同的优化问题设置小生境参数的最优值，以及如何有效地跳出局部最优。这两个难点在很大程度上限制了它们的实用性。本文在前人提出的鲸群算法（WSA）的基础上，提出了一种新的多峰优化算法WSA with Iterative Counter（WSA-IC）。WSA-IC一方面改进了WSA的迭代规则，避免了对不同问题指定不同的衰减系数以形成多个子种群的需要，同时也不引入任何小生境参数。另一方面，WSA-IC使得能够在迭代期间依赖于两个新参数（即，稳定性阈值Ts和适应性阈值Tf），以跳出所定位的极值点。同时证明了WSA-IC的收敛性。最后，在CEC 2015小生境基准测试函数和另外5个高维经典多峰函数上，将该算法与几种小生境元启发式算法进行了比较.实验结果表明，WSA-IC在统计上优于其他小生境元启发式算法对大多数测试函数。
Clustering of Local Optima in Combinatorial Fitness Landscapes
University of Nottingham（诺丁汉大学）；INRIA Lille - Nord Europe（法国国家信息与自动化研究所里尔-北欧分所）；University of Nice Sophia-Antipolis（尼斯索菲亚·安提波利斯大学）；University of Lausanne（洛桑大学）
Constrained Multi-Objective Genetic Algorithm Variants for Design and Optimization of Tri-Band Microstrip Patch Antenna loaded CSRR for IoT Applications: A Comparative Case Study
Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate
