Comprehensive Learning Strategy Research Articles

Memetic Variable Clustering and Its Application

Clustering analysis is an important and difficult task in data mining and big data analysis. Although being a widely used clustering analysis technique, variable clustering did not get enough attention in previous studies. Inspired by the metaheuristic optimization techniques developed for clustering data items, we try to overcome the main shortcoming of k-means-based variable clustering algorithm, which is being sensitive to initial centroids by introducing the metaheuristic optimization. A novel memetic algorithm named MCLPSO (Memetic Comprehensive Learning Particle Swarm Optimization) based on CLPSO (Comprehensive Learning Particle Swarm Optimization) has been studied under the framework of memetic computing in our previous work. In this work, MCLPSO is used as a metaheuristic approach to improve the k-means-based variable clustering algorithm by adjusting the initial centroids iteratively to maximize the homogeneity of the clustering results. In MCLPSO, a chaotic local search operator is used and a simulated annealing- (SA-) based local search strategy is developed by combining the cognition-only PSO model with SA. The adaptive memetic strategy can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the variable clustering criterion on several datasets compared with the original variable clustering method. Finally, for practical use, we also developed a web-based interactive software platform for the proposed approach and give a practical case study—analyzing the performance of semiconductor manufacturing system to demonstrate the usage.

Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

This article presents a novel comprehensive learning bat algorithm (CLBAT) for the optimal coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC) for damping electromechanical oscillations in multi-machine power systems considering a wide range of operating conditions. The CLBAT incorporates a new comprehensive learning strategy (CLS) to improve microbat cooperation; location updating is also improved to maintain the bats’ diversity and to prevent premature convergence through a novel adaptive search strategy based on relative travelled distance. In addition, the proposed elitist learning strategy speeds up convergence during the optimization process and drives the global best solution towards promising regions. The superiority of the CLBAT over other algorithms is demonstrated via several experiments and comparisons through benchmark functions. The developed algorithm ensures convergence speed, credibility, computational resources and optimal tuning of PSSs and SVCs of multi-machine systems under different operating conditions through eigenanalysis, nonlinear simulation and performance indices.

Enhancing the Performance of Cuckoo Search Algorithm with Multi-Learning Strategies

The Cuckoo Search (CS) is apt to be trapped in local optimum relating to complex target functions. This drawback has been recognized as the bottleneck of its widespread use. This paper, with the purpose of improving CS, puts forward a Cuckoo Search algorithm featuring Multi-Learning Strategies (LSCS). In LSCS, the Converted Learning Module, which features the Comprehensive Learning Strategy and Optimal Learning Strategy, tries to make a coordinated cooperation between exploration and exploitation, and the switching in this part is decided by the transition probability Pc. When the nest fails to be renewed after m iterations, the Elite Learning Perturbation Module provides extra diversity for the current nest, and it can avoid stagnation. The Boundary Handling Approach adjusted by Gauss map is utilized to reset the location of nest beyond the boundary. The proposed algorithm is evaluated by two different tests: Test Group A(ten simple unimodal and multimodal functions) and Test Group B(the CEC2013 test suite). Experiments results show that LSCS demonstrates significant advantages in terms of convergence speed and optimization capability in solving complex problems.

Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.

Particle swarm optimization based on dimensional learning strategy

Abstract In traditional particle swarm optimization (PSO) algorithm, each particle updates its velocity and position with a learning mechanism based on its personal best experience and the population best experience. The learning mechanism in traditional PSO is simple and easy to implement, but it suffers some potential problems, such as the phenomena of “oscillation” and “two steps forward, one step back”. Therefore, designing an effective learning strategy to avoid these two phenomena and to improve the search efficiency is an urgent issue for PSO research. This paper proposes a dimensional learning strategy (DLS) for discovering and integrating the promising information of the population best solution according to the personal best experience of each particle. Thereafter, a two-swarm learning PSO (TSLPSO) algorithm based on different learning strategies is proposed. One of the subpopulations constructs the learning exemplars by DLS to guide the local search of the particles, and the other subpopulation constructs the learning exemplars by the comprehensive learning strategy to guide the global search. 16 classic benchmark functions, 30 CEC2014 test functions, and 1 real-world optimization problem are used to test the proposed algorithm against with 5 typical PSO algorithms and 1 state-of-the-art differential evolution (DE) algorithm. The experimental results show that TSLPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of TSLPSO are also significantly improved.

Comprehensive Learning Strategies for the Development of Mind Theory for Deaf Children and Hearing Impaired

Modern studies aim to push children with hearing impairment to integrate into social life and communicate with others. On the other hand, to develop the theory of mind by integrating them with educational systems that ensure their optimal development in order to raise the level of their cognitive intelligence and academic achievement. This study will review the literature of modern strategies for the integration of Hearing impaired and deaf children in the educational process and determine the negative and positive actions of each strategy through a theoretical comparison of the results of each strategy and then propose a set of recommendations to reach a comprehensive strategy focused on the positive points and Are trying to eliminate the negative effects of previous strategies in order to make the integration of students with hearing impairment or deaf in the process of comprehensive education to ensure that the development of the theory of mind to them as normal as possible compared to their peers and on the other hand ensure the psychological structure Sound and scientific achievement that qualifies them to integrate into practical life.

Generalized Pareto Model Based on Particle Swarm Optimization for Anomaly Detection

Anomaly detection of time series has been widely used in various fields. Most detection methods depend either on assumptions about data distribution or manual threshold setting. If the assumption is incorrect, the effectiveness of detection technology will be greatly reduced. To deal with this problem, we propose a maximum likelihood estimation method based on particle swarm optimization for generalized Pareto model to detect outliers of time series, which can be called Generalized Pareto Model Based on Particle Swarm Optimization (GPMPSO). Because the generalized Pareto model is multidimensional, we introduce a comprehensive learning strategy to improve search ability of particle swarm algorithm. Due to the multiple peaks of the log-likelihood function of generalized Pareto model, we apply dynamic neighbors to reduce the possibility of particle swarm optimization falling into local optimum. Moreover, we propose a new processing model Big Drift Streaming Peak Over Threshold (BDSPOT) to enhance the capability of the data stream processor. Our algorithm is tested on various real-world datasets which demonstrate its very competitive performance.

A Heterogeneous Evolving Cuckoo Search Algorithm for Solving Large-Scale Combined Heat and Power Economic Dispatch Problems

Combined heat and power economic dispatch (CHPED) problem aims to optimally schedule the output of generating units with minimum fuel cost, which is a highly non-linear, non-convex and large-scale global optimization problem with many practical constraints. The complexity of the problem demands solution methods with powerful search ability, robustness, and computational efficiency. This paper proposes a heterogeneous evolving cuckoo search (HECS) algorithm with a novel constraint-handling mechanism to solve the large-scale CHPED problem considering valve-point effect. Based on the cuckoo search algorithm, we apply a comprehensive learning strategy to enhance the search ability in the high-dimensional environment, and a heterogeneous evolving strategy to improve the robustness of the algorithm. Moreover, we develop a novel constraint-handling mechanism that uses strict mathematical methods to repair unfeasible solutions and avoid redundant calculation. 5 tests are conducted on 24-unit, 48-unit, 84-unit, 96-unit, and 192-unit systems and the results are compared with the state-of-the-art algorithms published in the year 2015-2019. The comparisons show that the HECS could annually save millions of dollars in some large-scale systems, which verify its effectiveness.

Fractional-order controllers optimized via heterogeneous comprehensive learning pigeon-inspired optimization for autonomous aerial refueling hose–drogue system

Dynamic modeling and control system design for the hose–drogue system (HDS) in the docking stage of autonomous aerial refueling (AAR) are investigated in this paper. The dynamics and kinematics of hose are modeled via a finite-segment multi-body method, which describes the hose–drogue assembly as a link-connected system. A controllable drogue is connected to the hose for automatically stabilizing the drogue's relative position under the influences of tanker trailing vortex, receiver bow wave, atmospheric turbulence, gust, and wind shear. Thus, a drogue position control law based on fractional-order method is designed to resist the multi-wind disturbances. Noting that it is difficult to tune the parameters of fractional-order controller (FOC), a modified pigeon-inspired optimization (PIO), the hybrid of heterogeneous comprehensive learning strategy and PIO (HCLPIO), is carried out to optimize the parameters of FOC. The simulation results show that the proposed optimized fractional-order feedback controllers effectively stabilize the controllable drogue to swing within an acceptable range.

Adaptive particle swarm optimization with population diversity control and its application in tandem blade optimization

This paper proposes a new variant of particle swarm optimization, namely adaptive particle swarm optimization with population diversity control (APSO-PDC), to improve the performance of particle swarm optimization. APSO-PDC is formulated based on adaptive selection of particle roles, population diversity control, and adaptive control of parameters. The adaptive selection of particle roles which combines the evolutionary state and dynamic particle state estimation method will sort the particles into three roles to let different particles execute different search tasks during optimization process. The adaptive control of parameters which is created based on the evolutionary state and particle roles encourages the exploitation ability and enhances the algorithm’s convergence speed. The population diversity control which combines comprehensive learning strategy of the comprehensive learning particle swarm optimizer with evolutionary state to update the individual best position strengthens exploration ability and thus increases the algorithm’s robustness toward the premature convergence issue. The performance of APSO-PDC is comprehensively evaluated by 21 unimodal and multimodal functions with or without rotation. The results indicate APSO-PDC has more preferable searching accuracy, searching reliability, and convergence speed than the other well-established particle swarm optimization variants. Finally, compared with other six particle swarm optimization variants, APSO-PDC shows satisfactory performance in optimizing tandem blade. This excellent performance proves that APSO-PDC has a better control of swarm exploration and exploitation abilities.

A multiobjective hybrid bat algorithm for combined economic/emission dispatch

In this paper, a multiobjective hybrid bat algorithm is proposed to solve the combined economic/emission dispatch problem with power flow constraints. In the proposed algorithm, an elitist nondominated sorting method and a modified crowding-distance sorting method are introduced to acquire an evenly distributed Pareto Optimal Front. A modified comprehensive learning strategy is used to enhance the learning ability of population. Through this way, each individual can learn not only from all individual best solutions but also from the global best solutions (nondominated solutions). A random black hole model is introduced to ensure that each dimension in current solution can be updated individually with a predefined probability. This is not only meaningful in enhancing the global search ability and accelerating convergence speed, but particularly key to deal with high dimensional systems, especially large-scale power systems. In addition, chaotic map is integrated to increase the diversity of population and avoid premature convergence. Finally, numerical examples on the IEEE 30-bus, 118-bus and 300-bus systems, are provided to demonstrate the superiority of the proposed algorithm.

Optimization design for tandem cascades of compressors based on adaptive particle swarm optimization

ABSTRACTTo improve the flow performance of tandem cascades on design and off design incidence angle and increase the stable operation range, an optimization system for tandem cascades was developed based on an adaptive particle swarm optimization (APSO-PDC). Firstly, APSO-PDC was proposed based on adaptive selection of particle roles and population diversity control. The adaptive selection of particle roles which combines the evolutionary state and dynamic particle state estimation (DPSE) method will sort the particles into three roles to help different particles execute different search tasks during optimization process. The population diversity control, which combines comprehensive learning strategy of the comprehensive learning particle swarm optimizer (CLPSO) with evolutionary state, pretty strengthens the exploration ability and avoids falling into the local optima. The performance of APSO-PDC is evaluated by 11 unimodal and multimodal functions. Compared with the other six PSOs, the results indicate APSO-PDC has better performance in terms of algorithm accuracy and algorithm reliability. In addition, APSO-PDC is validated by optimizing two large-turning tandem cascades, including low-dimension (5 optimization variables) and high-dimension problems (34 optimization variables). Compared with the other six PSOs, the optimization results demonstrate APSO-PDC has the fastest convergence speed and simultaneously controls well the population diversity.

Erratum to: A multi-objective optimization method based on discrete bacterial algorithm for environmental/economic power dispatch

Multi-objective optimization is an interesting and hot topic in the literature involving the conflicting objectives to be solved simultaneously. In this study, a new multiple optimization method based on a discrete bacterial algorithm is developed to address the multi-objective economic-environmental dispatch problem with non-linear, non-convex, and complexity constraints. In the proposed multi-objective bacterial based algorithm, the existence of bacteria complies with a fitness survival mechanism, in which a health sorting approach is operated to control the chances of reproduction as well as elimination. The performances of bacteria have been recorded and sorted for health evaluation, which can help to group the individuals according to their search capability and improve the overall quality of the population. To speed up the convergence rate and avoid local minima to some extent, a comprehensive learning strategy is embedded to enable the communication exchanges between the bacteria and external archive. The standard IEEE 30-bus, 6-generator test system is adopted to illustrate the efficiency of the proposed method by making the comparison with the other multiple bacterial-based algorithms as well as six other well developed evolutionary algorithms. The effectiveness of the propose method is well validated in experiments by providing similar or superior solutions to environmental/economic power dispatch issues considering the various constraints.

Social Media Driven Management Education

Business schools are under growing pressure to engage in significant programmatic reforms in light of the business community's call for web-savvy, problem-solving graduates. Even AACSB has gotten into the reformation act by recommending the adoption of a comprehensive collaboration learning strategy. To meet these and related challenges, many schools of business are turning to social media to provide learning opportunities at a time and place that is convenient to the student. The purpose of this paper is to highlight the growing possibilities for using social media to enhance learning outcomes and to outline strategies for implementing this revolutionary process throughout the management education community of practice.

A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition

This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters’ impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.

Distance Entrepreneurship Education as An Essential Strategy to Empower Indonesian Migrant Workers

The purpose of the entrepreneurship training is to empower learners to be entrepreneurs and when this learning process is delivered by online learning it will create much more flexibility. The training can be delivered from the home country where there is no language barrier, it is cheaper than traditional face-to-face training, it can be accessed at any time, and the content is a homeland context. This study aims to explain the training process by distance learning which is administered by Ciputra University Entrepreneurship Centre at Hongkong. The training is then strengthened by ICT infrastructure, communication strategy and comprehensive learning strategy. The result shows that there is a significant change on the entrepreneurship skills of Indonesian workers, therefore the role of government is needed to to support this significant result (the involvement, government policies, campaign of distance learning, and the comprehensive making of user friendly hardware and software).

Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation

This paper presents a comprehensive learning particle swarm optimization algorithm with enhanced exploration and exploitation, named as “heterogeneous comprehensive learning particle swarm optimization” (HCLPSO). In this algorithm, the swarm population is divided into two subpopulations. Each subpopulation is assigned to focus solely on either exploration or exploitation. Comprehensive learning (CL) strategy is used to generate the exemplars for both subpopulations. In the exploration-subpopulation, the exemplars are generated by using personal best experiences of the particles in the exploration-subpopulation itself. In the exploitation-subpopulation, the personal best experiences of the entire swarm population are used to generate the exemplars. As the exploration-subpopulation does not learn from any particles in the exploitation-subpopulation, the diversity in the exploration-subpopulation can be retained even if the exploitation-subpopulation converges prematurely. The heterogeneous comprehensive learning particle swarm optimization algorithm is tested on shifted and rotated benchmark problems and compared with other recent particle swarm optimization algorithms to demonstrate superior performance of the proposed algorithm over other particle swarm optimization variants.

The Effect of Comprehensive Learning Strategies on Middle School Students' Learning English Words : A Case Study

The Effect of Comprehensive Learning Strategies on Middle School Students' Learning English Words : A Case Study

Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows

This paper proposes a variant of the bacterial foraging optimization (BFO) algorithm with time-varying chemotaxis step length and comprehensive learning strategy which we call adaptive comprehensive learning bacterial foraging optimization (ALCBFO). An adaptive non-linearly decreasing modulation model is used to keep a well balance between the exploration and exploitation of the proposed algorithm. The comprehensive learning mechanism maintains the diversity of the bacterial population and thus alleviates the premature convergence. Compared with the classical GA, PSO, the original BFO and two improved BFO (BFO-LDC and BFO-NDC) algorithm, the proposed ACLBFO shows significantly better performance in solving multimodal problems. We also assess the performance of the ACLBFO method on vehicle routing problem with time windows (VRPTW). Compared with three other BFO algorithms, the proposed algorithm is superior and confirms its potential to solve vehicle routing problem with time windows (VRPTW).

An improved CACO algorithm based on adaptive method and multi-variant strategies

Chaotic ant colony optimization (CACO) algorithm is an effective optimization algorithm that simulates the self-organization and chaotic behavior of ants. However, in the research and application of the CACO algorithm for solving complex optimization problems, the CACO algorithm presents some disadvantages. In order to resolve these disadvantages, an improved CACO algorithm based on adaptive multi-variant strategies (CACOAMS) is proposed in this paper. The CACOAMS algorithm takes full advantage of multi-population strategy, the neighborhood comprehensive learning strategy, the fine search strategy, the chaotic optimization strategy, the super excellent ant strategy, the punishment strategy and min---max ant strategy in order to avoid the local optimization solution and stagnation, guarantee learning rate of the different dimensions for each ant and the diversity of the search, eliminate the self-locking trap between environmental boundary and obstacles, improve the search efficiency, search accuracy and robustness of the algorithm. In order to testify to the performance of the CACOAMS algorithm, the CACOAMS algorithm is applied to test the benchmark functions and dynamically adjust the values of PID parameters. The simulation results show that the CACOAMS algorithm takes on the strong flexibility, adaptability and robustness. It can effectively improve system control precision and guarantee feasibility and effectiveness.