An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

Wu, Di; Wang, Shuang; Liu, Qingxin; Abualigah, Laith; Jia, Heming

doi:10.1155/2022/1535957

Cited by 29 publications

(17 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this part, we validate the performance of ERHHO on 23 benchmark functions [ 47 ] by comparing it with some state-of-art metaheuristics algorithms: SMA, WOA, SSA, SCA, and HHO, and HHO-based optimization algorithm: DHHO/M and HHOCM. Meanwhile, we use the Wilcoxon signed-rank test to acknowledge the differences between ERHHO and the comparative algorithms.…”

Section: Resultsmentioning

confidence: 99%

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Song

Jia

Abualigah

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Song

Jia

Abualigah

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this section, comparative experiments are conducted between EHHOCBO and six well-known hybrid algorithms based on IEEE CEC2017 test functions. The six algorithms are the Differential Squirrel Search Algorithm (DSSA) [54], Equilibrium Slime Mould Algorithm (ESMA) [55], Grey Wolf Optimizer Based on Aquila Exploration Method (AGWO) [56], Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy (RLTLBO) [57], Leader Harris Hawks optimization (LHHO) [58] and Hybrid Sine-Cosine Harris Hawks Optimization (SCHHO) [59]. IEEE CEC2017 test functions include 29 functions, which are widely used for performance testing and evaluation of intelligent bee swarm algorithms.…”

Section: Experiments 2: Ieee Cec2017 Test Functionsmentioning

confidence: 99%

Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems

Chen¹,

Guo²,

Xiao³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Harris Hawks Optimization (HHO) is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems. Nevertheless, the basic HHO algorithm still has certain limitations, including the tendency to fall into the local optima and poor convergence accuracy. Coot Bird Optimization (CBO) is another new swarm-based optimization algorithm. CBO originates from the regular and irregular motion of a bird called Coot on the water's surface. Although the framework of CBO is slightly complicated, it has outstanding exploration potential and excellent capability to avoid falling into local optimal solutions. This paper proposes a novel enhanced hybrid algorithm based on the basic HHO and CBO named Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization (EHHOCBO). EHHOCBO can provide higher-quality solutions for numerical optimization problems. It first embeds the leadership mechanism of CBO into the population initialization process of HHO. This way can take full advantage of the valuable solution information to provide a good foundation for the global search of the hybrid algorithm. Secondly, the Ensemble Mutation Strategy (EMS) is introduced to generate the mutant candidate positions for consideration, further improving the hybrid algorithm's exploration trend and population diversity. To further reduce the likelihood of falling into the local optima and speed up the convergence, Refracted Opposition-Based Learning (ROBL) is adopted to update the current optimal solution in the swarm. Using 23 classical benchmark functions and the IEEE CEC2017 test suite, the performance of the proposed EHHOCBO is comprehensively evaluated and compared with eight other basic meta-heuristic algorithms and six improved variants. Experimental results show that EHHOCBO can achieve better solution accuracy, faster convergence speed, and a more robust ability to jump out of local optima than other advanced optimizers in most test cases. Finally, EHHOCBO is applied to address four engineering design problems. Our findings indicate that the proposed method also provides satisfactory performance regarding the convergence accuracy of the optimal global solution.

show abstract

“…So, different search operators can be used in different generations, and the chance of getting stuck in a locally optimal solution can be lowered. Wu et al [52] proposed a modi cation to the teaching-learning-based optimization algorithm by incorporating Q-learning into the RLTLBO algorithm. First, the teacher phase of basic TLBO is completed.…”

Section: Chen Et Almentioning

confidence: 99%

Q-Learning based Metaheuristic Optimization Algorithms: A short review and perspectives

Hamad

Samma

Suandi

2023

Preprint

View full text Add to dashboard Cite

In recent years, reinforcement learning (RL) has garnered a great deal of interest from researchers because of its success in handling some complicated issues. Specifically, Q-learning as a model of RL is used a lot in various fields, and it has given an attractive result in games. In recent years, some researchers have tried to exploit the power of Q-learning to improve the results of optimization algorithms by guiding the optimization algorithm search agents based on the data saved in Q-table during the search process. The best search agent is chosen based on its accumulated performance, in other words, how well it has done overall, not how well it has done at each iteration. It is important to note that this review does not focus on reinforcement learning algorithms collaborating with metaheuristic optimization algorithms because there are so many reinforcement learning algorithms and to narrow the scope of the review, this paper will only discuss Q-learning used to enhance metaheuristic optimization algorithms. In this study will look at the huge progress made in the research community by looking at 32 different algorithms proposed on the subject from 2009 to 2022, with a focus on studies published in the last five years. As a result of the surveys conducted in this study, researchers (novices and experts) in the field of metaheuristic optimization algorithms research are expected to gain a better understanding of current research trends involving the use of Q-Learning and new motivations for outlining appropriate strategic plans for future development work as a result of the surveys conducted in this study.

show abstract

An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

Cited by 29 publications

References 63 publications

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems

Q-Learning based Metaheuristic Optimization Algorithms: A short review and perspectives

Contact Info

Product

Resources

About