# Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms
## 2 Overview of Published Articles
Experimental results reveal that the proposed hybrid approach outperforms the original multi-direction detection BAS algorithm, particularly in solving high-dimensional problems. The Cat Swarm Optimization (CSO) algorithm, introduced by [PERSON] et al. [9], has inspired a range of variations within the CSO family, widely utilized for solving continuous optimization problems. Building on this foundation, [PERSON] et al., in Contribution 6, present a compact Cat Swarm Optimization algorithm incorporating a Small-Sample Probability Model (SSPCCSO). Like other CSO algorithms, SSPCCSO employs both a tracking mode and a searching mode to identify optimal solutions. Additionally, it introduces a novel differential operator within the searching mode and leverages the gradient descent method. Experimental evaluations demonstrate that SSPCCSO outperforms other compact evolutionary algorithms in terms of solution quality and efficiency. Advancements in multi-core fiber technology have driven the adoption of spatial division multiplexing (SDM) within elastic optical networks (EONs). A key challenge in EONs is the routing and spectrum assignment (RSA) problem, which requires the contiguous and continuous allocation of spectrum across all links in a lightpath. The introduction of spatial dimensions further complicates this issue, transforming it into the routing, spectrum, and core allocation (RSCA) problem [10]. [PERSON] et al., in Contribution 7, enhance existing approaches to address the RSCA problem, formulating it as an integer linear programming model. They propose a joint routing and core coding scheme that incorporates core switching and introduce a multi-objective evolutionary algorithm based on decomposition, enhanced by adaptation and multi-strategy fusion. Simulation results indicate that their proposed algorithm outperforms existing methods by achieving more diverse and dominated solutions while effectively incorporating core-switching capabilities. The Software Module Clustering Problem (SMCP) is an optimization challenge focused on enhancing the modularity of software projects within the framework of Search-Based Software Engineering (SBSE) [11]. SBSE seeks to transition software engineering tasks from human-centric search methods to automated, machine-based approaches. This shift leverages techniques from metaheuristic search, operations research, and evolutionary computation paradigms [12]. [PERSON] and [PERSON], in Contribution 8, explore the multi-objective software module clustering problem, which entails assembling an optimal set of modules based on specified criteria to achieve improved modularity and functionality. Their experimental findings demonstrate that the proposed approach surpasses the performance of standalone multi-objective evolutionary algorithms. In Contribution 9, [PERSON] et al. introduce the Genetic Algorithm with the Symmetrization Operator (GASO), a memetic variation of the traditional genetic algorithm. This approach involves the cyclic symmetrization of the population, where the parental points are symmetrized around the current leader of the population. They analyze the algorithm, provide examples of its application, and evaluate its effectiveness on 24 continuous functions within a multidimensional cube using the Black-Box Optimization Benchmarking (BBOB-2010) benchmark. The proposed algorithm enhances the exploration of the search space, especially for test functions classified in the benchmark as high-conditioning and unimodal. Radial Basis Function (RBF) networks are a widely used type of feedforward artificial neural networks designed for function approximation tasks. These networks consist of three layers, with each performing specific functions: the input layer, the hidden layer, and the output layer [13]. In Contribution 10, [PERSON] et al. propose a two-phase hybrid approach to train RBF neural networks for classification and regression tasks.