In this paper, a bioinspired path planning approach for mobile robots is proposed. The approach is based on the sparrow search algorithm, which is an intelligent optimization algorithm inspired by the group wisdom, foraging, and anti-predation behaviors of sparrows. To obtain high-quality paths and fast convergence, an improved sparrow search algorithm is proposed with three new strategies. First, a linear path strategy is proposed, which can transform the polyline in the corner of the path into a smooth line, to enable the robot to reach the goal faster. Then, a new neighborhood search strategy is used to improve the fitness value of the global optimal individual, and a new position update function is used to speed up the convergence. Finally, a new multi-index comprehensive evaluation method is designed to evaluate these algorithms. Experimental results show that the proposed algorithm has a shorter path and faster convergence than other state-of-the-art studies.

A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm

Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning. 

https://link.springer.com/content/pdf/10.1007/s11831-022-09804-w.pdf

Advances in Sparrow Search Algorithm: A Comprehensive Survey

This paper solves the drawbacks of traditional intelligent optimization algorithms relying on 0 and has good results on CEC 2017 and benchmark functions, which effectively improve the problem of algorithms falling into local optimality. The sparrow search algorithm (SSA) has significant optimization performance, but still has the problem of large randomness and is easy to fall into the local optimum. For this reason, this paper proposes a learning sparrow search algorithm, which introduces the lens reverse learning strategy in the discoverer stage. The random reverse learning strategy increases the diversity of the population and makes the search method more flexible. In the follower stage, an improved sine and cosine guidance mechanism is introduced to make the search method of the discoverer more detailed. Finally, a differential-based local search is proposed. The strategy is used to update the optimal solution obtained each time to prevent the omission of high-quality solutions in the search process. LSSA is compared with CSSA, ISSA, SSA, BSO, GWO, and PSO in 12 benchmark functions to verify the feasibility of the algorithm. Furthermore, to further verify the effectiveness and practicability of the algorithm, LSSA is compared with MSSCS, CSsin, and FA-CL in CEC 2017 test function. The simulation results show that LSSA has good universality. Finally, the practicability of LSSA is verified by robot path planning, and LSSA has good stability and safety in path planning.

/pdf/a-learning-sparrow-search-algorithm-1geg0do3yy.pdf

A Learning Sparrow Search Algorithm.

A novel hybrid model for water quality prediction based on synchrosqueezed wavelet transform technique and improved long short-term memory

Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot’s trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area A highly efficient route planning approach is required for this complex high dimensional optimization problem However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm Finally, the Cauchy-Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration

https://www.mdpi.com/1424-8220/21/4/1224/pdf

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV.

Virus spreading problems in wireless rechargeable sensor networks (WSNs) are becoming a hot topic, and the problem has been studied and discussed in recent years. Many epidemic spreading models have been introduced for revealing how a virus spreads and how a virus is suppressed. However, most of them assumed the sensors are not rechargeable sensors. In addition, most of existing works do not consider virus mutation problems. This paper proposes a novel epidemic model, including susceptible, infected, variant, low-energy and dead states, which considers the rechargeable sensors and the virus mutation factor. The stability of the proposed model is first analyzed by adopting the characteristic equation and constructing Lyapunov functions methods. Then, an optimal control problem is formulated to control the virus spread and decrease the cost of the networks by applying Pontryagin’s maximum principle. Finally, all of the theoretical results are confirmed by numerical simulation.

Dynamics Analysis of a Wireless Rechargeable Sensor Network for Virus Mutation Spreading.

With the development of wireless sensor networks (WSNs), energy constraints and network security have become the main problems. This paper discusses the dynamic of the Susceptible, Infected, Low-energy, Susceptible model under pulse charging (SILS-P) in wireless rechargeable sensor networks. After the construction of the model, the local stability and global stability of the malware-free T-period solution of the model are analyzed, and the threshold R0 is obtained. Then, using the comparison theorem and Floquet theorem, we obtain the relationship between R0 and the stability. In order to make the conclusion more intuitive, we use simulation to reveal the impact of parameters on R0. In addition, the paper discusses the continuous charging model, and reveals its dynamic by simulation. Finally, the paper compares three charging strategies: pulse charging, continuous charging and non-charging and obtains the relationship between their threshold values and system parameters.

https://www.mdpi.com/1099-4300/23/8/927/pdf

Zhongwei Liang

Papers

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV.

Dynamics Analysis of a Wireless Rechargeable Sensor Network for Virus Mutation Spreading.

Modelling and Analysis of the Epidemic Model under Pulse Charging in Wireless Rechargeable Sensor Networks.