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.

scholarly journals Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 225
Author(s):  
Chong Zhou ◽  
Shengjie Li ◽  
Yuhe Zhang ◽  
Zhikun Chen ◽  
Cuijun Zhang
Keyword(s):  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Population Based ◽  
Population Diversity ◽  
Convergence Speed ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Current Population ◽  
Unit Simplex ◽  
Mutation Strategy

Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.

scholarly journals A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 291
Author(s):  
Wenbiao Yang ◽  
Kewen Xia ◽  
Tiejun Li ◽  
Min Xie ◽  
Fei Song
Keyword(s):  
Extreme Learning Machine ◽  
Learning Strategy ◽  
Classification Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Control Factor ◽  
Initial Population ◽  
Local Optimum ◽  
Marine Predator ◽  
Learning Machine

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

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.

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