Search

Course scheduling is a combinatorial optimization problem with multiple constraints. To achieve the reasonable allocation of teaching resources, all courses should be arranged under a number of specified constraints. With the increased number of courses, the solution space increases exponentially. However, the traditional methods cannot quickly find the optimal solution. In order to overcome the disadvantages of low efficiency and the possibility of high conflict in traditional course scheduling, this research study proposes a course scheduling, method based on an improved binary cuckoo search algorithm (IBCS). First of all, a multi-objective and multi-constraint mathematical model of course scheduling is established. Course scheduling involves issues, such as allocating teachers, courses, classes, classrooms and time under specific constraints. The class element is defined to represent the class and the course that the teachers will teach. Therefore, the problem of course scheduling is transformed into the process of mapping from the class element to the classroom-time pair. Six hard constraints and three soft constraints are also defined accordingly. Then, the BCS algorithm is used to search the best course scheduling scheme in the binary-encoded solution space. In order to control the convergence rate effectively and avoid falling into the local optimum, a dynamically adjustable, equilibrium coefficient is introduced. Therefore, the algorithm retains the diversity of the solutions while converging. Finally, this research study constructs the data set and carries out the simulation experiment. It also compares the IBCS algorithm in this paper with the genetic algorithm and the standard binary cuckoo search algorithm (BCS). Experimental results show that the IBCS algorithm can converge in effective time. Meanwhile, it can achieve an improved global scheduling scheme with higher stability. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of complex image background and difficulties in the later image segmentation, an image segmentation algorithm based on the chaotic particle swarm algorithm and fuzzy clustering is proposed. First, the color space is converted from the RGB color space into the HIS color space. Then, a hybrid algorithm consisted of chaotic particle swarm optimization and fuzzy clustering is introduced. Each color component is processed by the algorithm, and the corresponding partition graph is obtained. Finally, the color space is converted into the RGB color space to achieve the segmentation effect. Experimental results show that the new algorithm has higher accuracy to segment the image and has good robustness to noises. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) becomes more and more intelligent, a new computing paradigm, predictive intelligence is incorporated into many IoT applications. The devices of predictive intelligence in IoT applications must consider the power and delay consumption. As the Power-Driven X-Routing (PDXR) problem model under the advanced semiconductor design, the length-restricted condition in the multi-dynamic voltage model is introduced to save power consumption and the X-architecture is introduced to better reduce the wirelength to optimize the chip delay. To this end, an effective particle swarm optimization-based power-driven length-restricted X-routing algorithm is proposed for predictive intelligence in IoT applications. Firstly, a pre-calculated lookup table is designed to provide fast information query for the subsequent algorithm flow. Secondly, an improved particle swarm optimization algorithm is presented for the discrete PDXR problem. Thirdly, in the adjustment phase, the choice of intermediate nodes is expanded, and is no longer limited to the corner points of obstacles. Fourthly, a removal strategy of redundant points is proposed to optimize the routing path. Finally, the wirelength is further reduced by a local topology optimization strategy. The experimental results show that the proposed algorithm can achieve the best wirelength cost at a very fast speed under the constraint of restricted wirelength, so as to better satisfy the demand of the power and delay performance of semiconductor design for predictive intelligence in IoT applications. [Display omitted] • An improved PSO algorithm is presented for discrete routing problem. • A preprocessing technology is designed to construct initial routing tree. • An adjustment strategy is proposed to correct edges that violate constraints. • An optimization system is developed to further optimize wirelength. [ABSTRACT FROM AUTHOR]

Prediction problems are difficult to be carried out in a dynamic environment, for two key questions: one is how to monitor environmental changes, the other is how to make respond timely after the environment has changed. Multi-population strategy is often adopted to address both two key problems. However, there are two stubborn questions that limit and affect the effectiveness of the strategy, i.e., (1) Search overlaps are easy to occur which lead to lose the ability of local exploit, (2) In the course of evolution, the search range of subpopulations tends to assimilate, so the subpopulation will gradually lose the ability to explore whole search space. Therefore, this paper adopts multi-population strategy, and presents a novel intelligence algorithm based on particle swarm optimization to solve the above problem, called adaptively reversed diffusion dual-drive evolutionary algorithm (ARDDEA). (1) Firstly, ARDDEA monitors the environmental changes by setting the global dynamic sentry in each subgroup. (2) Secondly, in order to avoid searching overlapping of the sub-population, a new exclusion strategy is proposed in this paper. A new distance determination method, i.e., between-swarms average Mahalanobis​ distance, is devised in the exclusion strategy to decide the inter-population distance. If the distance is too small between two sub-populations, furtherly, a Hill–valley decision function is used to determine whether they tracked the same peak or not. If so, the inferior subpopulations will be reinitialized by a reverse diffusion operation (RD) proposed in this paper. Besides, (3) a new dual-drive kinetic updating equation is proposed to enhance the search capability of the population. The new algorithm compared with several state-of-art dynamic optimization algorithms on the moving peak problem. The results show that the ARDDEA algorithm can track the optimal solution more effectively in the dynamic environment, and shows strong robustness and adaptability. It is a hope algorithm applied to prediction problems. • A novel dual-drive kinetic updating formula is proposed. • A between-swarms average Mahalanobis distance is devised in the exclusion strategy. • A Hill-valley decision is used to judge whether the two subpopulations overlap. • A reverse diffusion operation is proposed to reinitialize subpopulation. [ABSTRACT FROM AUTHOR]

One of the core features of Intelligent Edge Computing (IEC) is real-time decision making, therefore low delay is more important for IC design under IEC architecture. And in very large scale integration routing, wirelength is one of the most important indexes affecting the final delay of the IC design. Therefore, this paper introduces the X-routing with more potential for wirelength optimization and the Steiner Minimum Tree (SMT), which is the best routing model in multi-terminal nets. Then, based on Particle Swarm Optimization (PSO) technique which has the strong global optimization ability in Soft Computing, an effective Two-Stage X-routing Steiner minimum tree construction algorithm is proposed. The proposed algorithm is divided into two stages: social learning discrete PSO searching and wirelength reduction. In the first stage, two excellent strategies are proposed to maintain a good balance between exploration and exploitation capabilities of the PSO technique: (1) Chaotic decreasing inertia weight combined with mutation operator is set to enhance the exploration capability. (2) A new social learning approach combined with crossover operator is designed to ensure the diverse evolution of the swarm while maintaining the exploitation capability. In the second stage, a strategy based on local topology optimization is proposed to further reduce the length of X-routing Steiner tree. Experiments show that the proposed algorithm can achieve the best wirelength optimization and has a strong stability, especially for large-scale SMT problem, so as to better satisfy the demand of low delay of IC design under IEC architecture. [Display omitted] • Better satisfying the high-performance requirement for IC design under IEC. • Being divided into two stages to obtain routing scheme with shortest wirelength. • Presenting a novel Social Learning DPSO method for SMT problem. • Being validated with benchmark circuit suite. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) and its variants are often used to train and optimize the structure and parameters of deep learning models to improve accuracy of learning results in the reasonable time-consuming. The performance of PSO is completed determined by its kinetic equation of particles. To accelerate convergence rate, a novel kinetic equation without inertial term is devised and applied to PSO, and then a non-inertial opposition-based particle swarm optimization (NOPSO) is generated combined with a adaptive elite mutation strategy and generalized opposition-based learning strategy. Simulation Experimental results show that the new kinetic equation has effectively accelerated convergence rate of PSO. Meanwhile, Theoretical analysis of the new kinetic equation is carried out by order-2 difference recurrence equation, the inference conclusions of which are consistent with the results of simulation experiments. NOPSO algorithm with a new kinetic equation is a highly competitive algorithm compared with some state-of-art PSOs and is suitable for deep learning applications. • A novel velocity equation formula(NIV) inspired by human behavior is devised. • Theoretical analysis of NIV is carried out and proves its validity. • A non-intertial opposition-based PSO algorithm(NOPSO) with NIV is proposed. [ABSTRACT FROM AUTHOR]

This paper carries out the mechanism analysis of non-inertial particle swarm optimization. It is a modified algorithm based on a new kinetic equation which is applied to the prediction of non-stationary time series for the internet of things in the edge computing. In order to avoid premature convergence and accelerate convergence rate, different from standard particle swarm optimization, the modified algorithm uses a new kinetic equation to lead particles motion direction, besides generalized opposition-based learning (GOBL) and adaptive elite mutation (AEM) strategies. The work presents the second order standardized recurrence equation for the new kinetic equation whose corresponding characteristic equations can be analyzed via the difference functions to obtain the boundaries of coefficients and its convergence region. Besides, GOBL and AEM strategies are also analyzed to boost global and local convergence of the algorithm as an interpretation. It is illustrated that performance analysis of the algorithm with two well-known test functions. The good performance is further validated from application in edge computing. [ABSTRACT FROM AUTHOR]