Improving Ant Colony Optimization efficiency for solving large TSP instances

A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems

With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimization algorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.