# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Intelligent Algorithm
#### 4.3.2 Particle Swarm Optimization (PSO)  
The Particle Swarm Optimization (PSO) algorithm was initially proposed by electrical engineer [PERSON] and social psychologist [PERSON] in 1995. The algorithm is inspired by the social behavior of bird flocking, aiming to simulate how organisms find optimal solutions through group cooperation [72; 73]. In 1998, [PERSON] and [PERSON] further extended and improved the PSO algorithm, introducing several enhanced versions, such as the inertia weight and constriction factor. These improvements significantly enhanced the algorithm's convergence speed and search capabilities [74; 75]. After entering the 2010s, research on the PSO algorithm deepened further, resulting in many variants and improved versions. These new versions often incorporate other algorithms, such as genetic algorithms, Ant Colony Algorithms, etc., to form hybrid optimization algorithms, improving performance and adaptability [73]. The fundamental idea of PSO originates from nature; when a flock of birds discovers a new feeding area, at the beginning, each bird heads towards places where it believes food might exist and notes the places where it sees the most food during the search process. When the flock comes together, they share the information they have found, allowing them to know where the most food currently is. Each bird then re-searches based on the information it has gathered and the information shared by the team. After several rounds of sharing, the flock can find the place with the most food, as shown in Figure 9.  
PSO has strong dynamic environmental adaptability, which makes it widely used in path planning based on high-dimensional maps. In the field of construction, it can help with path-planning for inspection drones, fireproof coating robots, etc. PSO has some drawbacks, including getting stuck in local optimal solutions in multi-dimensional spaces, slow convergence speed in the late stages of optimization, and sensitivity to parameter settings. To address these issues, researchers have proposed many improved Particle Swarm Optimization (PSO) algorithms. [PERSON] proposed an Improved Particle Swarm Optimization algorithm (IPSO) that enhances the algorithm's exploration ability and search accuracy by improving the velocity update method and inertia weight. It also employs a particle initialization strategy to increase population diversity and introduces strategies to deal with local optima, thereby shortening the convergence path length. Experimental data indicate that IPSO outperforms other algorithms in terms of average path length and shortest path length [76]. [PERSON] addressed the shortcomings of traditional Particle Swarm Optimization (PSO) algorithms in path planning, such as slow convergence speed and a tendency to get stuck in local optima, by proposing an improved Particle Swarm Optimization algorithm based on Differential Evolution (IPSO-IDE). This algorithm enhances the convergence speed of traditional PSO by introducing adaptive adjustments to the weights and acceleration coefficients. It also adds a leader particle to the swarm, selected through a voting mechanism to guide the group towards the optimal position. To overcome the deficiencies of traditional Differential Evolution algorithms, [PERSON] proposed an optimization method for the adaptive scaling factor F and the crossover probability factor CR to improve the convergence performance and iteration accuracy of the algorithm. Combining the improved Particle Swarm Optimization algorithm with Differential Evolution, a new hybrid optimization mechanism was proposed. This mechanism incorporates the concept of \"mutual benefit and win-win\", meaning that the two algorithms are in a cooperative relationship, optimizing each other to enhance their performance, thereby improving the overall performance of the swarm [77]. [PERSON] introduced an innovative Particle Swarm algorithm that incorporates the principles of artificial potential fields, referred to as the apfrPSO.