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.

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

With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging and reinfection process of WRSNs is proposed. Subsequently, the local and global stabilities of disease-free and epidemic equilibrium points are analyzed and simulated after obtaining the basic reproductive number R0. Detailedly, the simulations further reveal the unique characteristics of SILS when it tends to being stable, and the relationship between the charging rate and R0. Furthermore, the attack-defense game between malware and WRSNs is constructed and the optimal strategies of both players are obtained. Consequently, in the case of R0 1, the validity of the optimal strategies is verified by comparing with the non-optimal control group in the evolution of sensor nodes and accumulated cost.

A Novel Epidemic Model for Wireless Rechargeable Sensor Network Security

Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need to be addressed urgently. After considering the charging process, the activating anti-malware program process, and the launching malicious attack process in the modeling, the susceptible–infected–anti-malware–low-energy–susceptible (SIALS) model is proposed. Through the method of epidemic dynamics, this paper analyzes the local and global stabilities of the SIALS model. Besides, this paper introduces a five-tuple attack–defense game model to further study the dynamic relationship between malware and WRSNs. By introducing a cost function and constructing a Hamiltonian function, the optimal strategies for malware and WRSNs are obtained based on the Pontryagin Maximum Principle. Furthermore, the simulation results show the validation of the proposed theories and reveal the influence of parameters on the infection. In detail, the Forward–Backward Sweep method is applied to solve the issues of convergence of co-state variables at terminal moment.

Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model

Based on the traditional propagation model, this paper innovatively divides nodes into high- and low-energy states through introducing Low-energy (L) state and presents a whole new propagation model which is more suitable for WSNs (wireless sensor networks) against malicious programs, namely, SILRD (Susceptible, Infected, Low-energy, Recovered, Dead) model. In this paper, nodes are divided into five states according to the residual energy and infection level, and the differential equations are constructed to describe the evolution of nodes. At the same time, aiming at the exhaustion of WSNs’ energy, this paper introduces charging as a method to supplement the energy. Furthermore, we regard the confrontation between WSNs and malicious programs as a kind of game and find the optimal strategies by using the Pontryagin Maximum Principle. It is found that charging as a defense mechanism can inhibit the spread of malicious programs and reduce overall costs. Meanwhile, the superiority of bang-bang control on the SILRD model is highlighted by comparing with square control.

/pdf/differential-games-of-rechargeable-wireless-sensor-networks-4e2hmshiw3.pdf

Differential Games of Rechargeable Wireless Sensor Networks against Malicious Programs Based on SILRD Propagation Model

As energy-harvesting wireless sensor networks (EHWSNs) are increasingly integrated with all walks of life, their security problems have gradually become hot issues. As an attack means, malicious programs often attack sensor nodes in critical locations in the networks to cause paralysis and information leakage of the networks, resulting in security risks. Based on the previous works and the introduction of solar charging, we proposed a novel model, namely, Susceptible-Infected-Low (energy)-Recovered-Dead (SILRD) with solar energy harvesters. Meanwhile, this paper takes Logistic Growth as the drop rate of sensor nodes and the infection rate of multitype malicious programs under nonlinear condition into consideration. Finally, an - Susceptible-Infected-Low (energy)-Recovered-Dead ( SILRD) model is proposed. Based on the Pontryagin Maximum Principle, this paper proposes the optimal strategies based on the SILRD with solar energy harvesters and the SILRD. The effectiveness of SILRD with solar energy harvesters was demonstrated by comparison with the general epidemic model. At the same time, by analyzing different charging strategies, we conclude that solar charging is highly efficient. Moreover, we further analyze the influence of controllable and uncontrollable input and various node degrees on SILRD model.

/pdf/attack-defense-game-between-malicious-programs-and-energy-4aenjndv8a.pdf

Baihao Peng

Papers

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

A Novel Epidemic Model for Wireless Rechargeable Sensor Network Security

Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model

Differential Games of Rechargeable Wireless Sensor Networks against Malicious Programs Based on SILRD Propagation Model

Attack-Defense Game between Malicious Programs and Energy-Harvesting Wireless Sensor Networks Based on Epidemic Modeling