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Human learning optimization (HLO) is a simple yet powerful metaheuristic developed based on a simplified human learning model. Competition and cooperation, as two basic modes of social cognition, can motivate individuals to learn more efficiently and improve their efficiency in solving problems by stimulating their competitive instincts and increasing interaction with each other. Inspired by this fact, this paper presents a novel human learning optimization algorithm with competitive and cooperative learning (HLOCC), in which a competitive and cooperative learning operator (CCLO) is developed to mimic competition and cooperation in social interaction for enhancing learning efficiency. The HLOCC can efficiently maintain the diversity of the algorithm as well as achieve the optimal values, demonstrating that the proposed CCLO can effectively improve algorithm performance. HLOCC has been compared with other heuristic algorithms on CEC2017 functions. In the second study, the uncapacitated facility location problems (UFLPs) which are one of the pure binary optimization problems are solved with HLOCC. The experimental results show that the developed HLOCC is superior to previous HLO variants and other metaheuristics with its improved exploitation and exploration abilities. [ABSTRACT FROM AUTHOR]

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems. [ABSTRACT FROM AUTHOR]

Highlights • The role of the random learning operator (RLO) in HLO is deeply studied and analyzed. • A novel adaptive strategy is developed based on the insightful understanding of RLO. • An improved adaptive HLO (IAHLO) is proposed and applied to engineering optimization problems. • The results on the engineering problems demonstrate the advantages of the proposed IAHLO. Abstract Human learning Optimization (HLO) is an emergent promising meta-heuristic algorithm which uses the random learning operator, the individual learning operator, and the social learning operator to search out the optimal solution of problems based on a simplified human learning model. To enhance the search performance, a new improved adaptive human learning optimization algorithm (IAHLO) is proposed in this paper, in which a novel adaptive strategy is developed to dynamically tune the control parameter of the random learning operator so that IAHLO can efficiently fasten the convergence at the beginning of iteration, develop the diversity at the middle of searching process to better explore the solution space, and perform the accurate local search at the end of search to find the optima. The presented IAHLO is applied to solve well-known engineering optimization problems and its results are compared with those of the standard HLO, adaptive HLO variants, and other metaheuristics to evaluate its performance. The comparison results show that IAHLO outperforms the other algorithms and achieves the best results on the engineering problems as the improved adaptive mechanism possesses the advantages of the linearly increasing and decreasing strategies and avoids their disadvantages. [ABSTRACT FROM AUTHOR]

Human Learning Optimization is a simple but efficient meta-heuristic algorithm in which three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to efficiently search the optimal solution by imitating the learning mechanisms of human beings. However, HLO assumes that all the individuals possess the same learning ability, which is not true in a real human population as the IQ scores of humans, one of the most important indices of the learning ability of humans, follow Gaussian distribution and increase with the development of society and technology. Inspired by this fact, this paper proposes a Diverse Human Learning Optimization algorithm (DHLO), into which the Gaussian distribution and dynamic adjusting strategy are introduced. By adopting a set of Gaussian distributed parameter values instead of a constant to diversify the learning abilities of DHLO, the robustness of the algorithm is strengthened. In addition, by cooperating with the dynamic updating operation, DHLO can adjust to better parameter values and consequently enhances the global search ability of the algorithm. Finally, DHLO is applied to tackle the CEC05 benchmark functions as well as knapsack problems, and its performance is compared with the standard HLO as well as the other eight meta-heuristics, i.e. the Binary Differential Evolution, Simplified Binary Artificial Fish Swarm Algorithm, Adaptive Binary Harmony Search, Binary Gravitational Search Algorithms, Binary Bat Algorithms, Binary Artificial Bee Colony, Bi-Velocity Discrete Particle Swarm Optimization, and Modified Binary Particle Swarm Optimization. The experimental results show that the presented DHLO outperforms the other algorithms in terms of search accuracy and scalability. [ABSTRACT FROM AUTHOR]

Inspired by human learning mechanisms, a novel meta-heuristic algorithm named human learning optimization (HLO) is presented in this paper in which the individual learning operator, social learning operator, random exploration learning operator and re-learning operator are developed to generate new solutions and search for the optima by mimicking the human learning process. Then HLO is applied to solve the well-known 5.100 and 10.100 multi-dimensional knapsack problems from the OR-library and the performance of HLO is compared with that of other meta-heuristics collected from the recent literature. The experimental results show that the presented HLO achieves the best performance in comparison with other meta-heuristics, which demonstrates that HLO is a promising optimization tool. [ABSTRACT FROM AUTHOR]

Online anomaly detection for stream data has been explored recently, where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation. However, due to the inherent complex characteristics of stream data, such as quick generation, tremendous volume and dynamic evolution distribution, how to develop an effective online anomaly detection method is a challenge. The main objective of this paper is to propose an adaptive online anomaly detection method for stream data. This is achieved by combining isolation principle with online ensemble learning, which is then optimized by statistic histogram. Three main algorithms are developed, i.e., online detector building algorithm, anomaly detecting algorithm and adaptive detector updating algorithm. To evaluate our proposed method, four massive datasets from the UCI machine learning repository recorded from real events were adopted. Extensive simulations based on these datasets show that our method is effective and robust against different scenarios. [ABSTRACT FROM AUTHOR]

Human Learning Optimization (HLO) is a simple yet powerful meta-heuristic developed based on a simplified human learning model. Many cognitive activities of humans contain an element of reasoning, and with reasoning, humans can gain deeper information on problems to boost learning performance. Inspired by this fact, this paper proposes a novel human learning optimization algorithm with reasoning learning (HLORL), in which a social reasoning learning operator (SRLO) is developed by using multiple social information sources to improve the global search ability of the algorithm. A parameter study is performed to give the recommended values of the control parameters. It also analyzes and discusses the role and function of the social reasoning learning operator. Finally, the proposed HLORL is applied to solve the CEC14 benchmark functions and 0-1 knapsack problems. The performance of HLORL is compared with the previous HLO variants and other state-of-art meta-heuristics. The experimental results demonstrate that the proposed HLORL has significant advantages over the compared algorithms. • This paper proposes a novel human learning optimization algorithm with reasoning learning (HLORL). • A social reasoning learning operator is developed by using multiple social information sources. • The role and function of the social reasoning learning operator is deeply analyzed and discussed. • The results demonstrate that the proposed HLORL has significant advantages over the previous HLO variants. [ABSTRACT FROM AUTHOR]

In this paper, under a general framework of Networked two-layer Learning Control Systems (NLCSs), optimal and fair network scheduling is studied. Multi-networked feedback control loops called subsystems in an NLCS share a common communication media. Therefore, there is a competition for the available bandwidth. A non-cooperative game fairness model is first formulated, and then the utility function of subsystems is designed. This takes into account of a number of factors, namely transmission data rate, sampling, the feature of scheduling pattern adopted, and networked control. For the problem defined above, the existence and uniqueness of Nash equilibrium point are proved. Following this, an evolutionary algorithm appeared in the literature recently, shuffled frog leaping algorithm, is improved applying to obtain an optimal solution. The algorithm has a high convergence rate, and the comparison simulation results have demonstrated the effectiveness of the proposed theoretical approach and the algorithm applied. [ABSTRACT FROM AUTHOR]

Humans perform Bayesian inference in a wide variety of tasks, which can help people make selection decisions effectively and therefore enhances learning efficiency and accuracy. Inspired by this fact, this paper presents a novel human learning optimization algorithm with Bayesian inference learning (HLOBIL), in which a Bayesian inference learning operator (BILO) is developed to utilize the inference strategy for enhancing learning efficiency. The in-depth analysis shows that the proposed BILO can efficiently improve the exploitation ability of the algorithm as it can achieve the optimal values and retrieve the optimal information with the accumulated search information. Besides, the exploration ability of HLOBIL is also strengthened by the inborn characteristics of Bayesian inference. The experimental results demonstrate that the developed HLOBIL is superior to previous HLO variants and other state-of-art algorithms with its improved exploitation and exploration abilities. [ABSTRACT FROM AUTHOR]

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. [Copyright &y& Elsevier]