# Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms
## 2 Overview of Published Articles  
Twelve articles are published in this Special Issue. They present novel results in the broad area of evolutionary and bioinspired optimization algorithms and their application in diverse domains, including the prediction of coconut yields, the optimization of laser-cutting paths, software clustering, and truck-drone parcel delivery, to name a few. In Contribution 1, [PERSON] et al. propose a robust and precise model for predicting coconut yields, leveraging the Bi-directional Long Short-Term Memory (BILSTM) deep learning classifier. This model is designed to facilitate informed decision making in coconut farming and agricultural research. To address the issue of overfitting, the model's hyperparameters are fine-tuned using the Levy Flight and Seagull Optimization Algorithm (LFSOA). Experimental results demonstrate that the proposed approach outperforms existing methods, achieving superior accuracy, precision, recall, F1-scores, and other statistical metrics. A conformal antenna was designed to fit a surface shaped based on considerations other than electromagnetic requirements, such as aerodynamic or hydrodynamic factors [2]. During its manufacturing process, a high-resolution industrial camera is used to detect the precise positions of feature points, facilitating tasks like coating, mounting, welding, and automated optical inspection. The challenge of path detection for this purpose can be modeled as a constrained three-dimensional traveling salesman problem (TSP). Toaddress this, [PERSON] et al., in Contribution 2, introduce the concept of a historical optimal population, where the optimal individual is added to the historical optimal population each time the fitness improves. Based on this concept, they develop an improved genetic algorithm (CHOP-IGA). Experimental results demonstrate that the CHOP-IGA significantly enhances path planning efficiency for conformal antenna surface detection, achieving a 48.44% improvement in detection efficiency compared to traditional genetic algorithms. Flocking, the coordinated and aligned movement of a group of individuals--such as birds or fish--appearing to behave as a unified super-organism, is a common phenomenon in nature [3]. Drawing inspiration from this natural behavior, [PERSON] introduced the concept to computer science [4]. Building on this idea, [PERSON] et al. [5] proposed modeling such groups as complex networks, where individuals are represented as nodes, and their interactions as edges are governed with simple, uniform rules. Expanding on these approaches, [PERSON] et al., in Contribution 3, develop two models that incorporate more realistic interaction mechanisms between individuals and the group during the flocking process. Simulation experiments demonstrate that these models significantly enhance the group's alignment efficiency, even under noisy conditions. In laser-cutting processes for manufacturing sheet metal products, optimizing the cutting path to minimize the total time required for cutting all components from the sheet is critical. This challenge is known as the cutting path problem [6]. [PERSON] et al., in Contribution 4, explore an adaptive biased random-key genetic algorithm (ABRKGA) combined with a heuristic to address this issue. Experimental results demonstrate that the proposed method outperforms existing approaches, delivering superior solution quality and reduced computational time. The Beetle Antennae Search (BAS) algorithm is a metaheuristic optimizer inspired by the foraging behavior of beetles [7]. Similarly, the Artificial Fish Swarm Algorithm (AFSA) draws inspiration from the schooling behavior of fish in nature and has seen significant advancements since its introduction in 2002, with numerous improved and hybrid models being developed [8]. In Contribution 5, [PERSON] et al. combine elements of BAS and AFSA, introducing a mutation mechanism and a multi-step detection strategy to enhance the BAS algorithm's optimization accuracy.