scholarly journals A Learning Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Chengtian Ouyang ◽  
Donglin Zhu ◽  
Fengqi Wang
Keyword(s):  
Path Planning ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Method ◽  
Search Process ◽  
Local Optimum ◽  
Test Function ◽  
Intelligent Optimization Algorithms ◽  
Robot Path

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.

scholarly journals Lens Learning Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chengtian Ouyang ◽  
Donglin Zhu ◽  
Yaxian Qiu
Keyword(s):  
Path Planning ◽  
Simulated Annealing Algorithm ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Improved Algorithm

In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

scholarly journals Adaptive Spiral Flying Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chengtian Ouyang ◽  
Yaxian Qiu ◽  
Donglin Zhu
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Stable Path ◽  
New Type ◽  
The Individual ◽  
Swarm Intelligence Optimization ◽  
Robot Path

The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.

scholarly journals Failure-Robot Path Complementation for Robot Swarm Mission Planning

2019 ◽  
Vol 9 (18) ◽  
pp. 3756 ◽  
Author(s):  
Meng-Tse Lee ◽  
Bo-Yu Chen ◽  
Wen-Chi Lu
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Unmanned Vehicles ◽  
Target Point ◽  
Diversification Strategy ◽  
Robot Swarm ◽  
Limited Power ◽  
Time Distance ◽  
Global Optimal ◽  
Robot Path

Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited.

scholarly journals Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhao ◽  
Zhicheng Jia ◽  
Lei Chen ◽  
Yanju Guo
Keyword(s):  
Learning Strategy ◽  
Search Algorithm ◽  
Population Control ◽  
Pso Algorithm ◽  
Convergence Speed ◽  
Evolutionary Equation ◽  
Control Factor ◽  
Local Optimum ◽  
Backtracking Search ◽  
Backtracking Search Algorithm

Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local optimum. In addition, enlightened by differential evolution (DE) algorithm, this article proposes a novel evolutionary equation based on the fact that the disadvantaged group will search just around the best individual chosen from previous iteration to enhance the ability of local search. Simulation experiments based on a set of 18 benchmark functions show that, in general, COBSA displays obvious superiority in convergence speed and convergence precision when compared with BSA and the comparison algorithms.

Path Planning for Mobile Robot with Clonal Selection Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2943-2946
Author(s):  
Yi Li ◽  
Zhen Hui Song ◽  
Li Zhao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Clonal Selection ◽  
Optimal Solution ◽  
Clonal Selection Algorithm ◽  
Mutation Operator ◽  
Robot Path Planning ◽  
Selection Algorithm ◽  
Evolutionary Progress ◽  
Robot Path

Aiming at the problem of path planning for a mobile robot, an oriented clonal selection algorithm is proposed. Firstly, the static environment was expressed by a map with nodes and links. Secondly, the locations of target and obstacles were defined. Thirdly, an oriented mutation operator was used to accelerate the evolutionary progress. In this way, we can find an optimal solution with proposed oriented clonal algorithm. Experiment results demonstrate that the algorithm is simple, effective, to solve the problem of robot path planning in a static environment

scholarly journals SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems.  This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.  

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