Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search(HS)algorithm is highly effective in solving a wide range of real-world engineering optimization problems.However,it still has the problems such as being prone to local optima,low optimization accuracy,and low search efficiency.To address the limitations of the HS algorithm,a novel approach called the Dual-Memory Dynamic Search Harmony Search(DMDS-HS)algorithm is introduced.The main innovations of this algorithm are as follows:Firstly,a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory,creating an effective and selectable trust region to reduce approach blind searching.Furthermore,the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability.Secondly,to boost the algorithm’s convergence speed,a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy.Lastly,the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm’s search strategies,which aim to rationalize the abilities of exploration and exploitation of the algorithm.The results tested on the Computational Experiment Competition on 2017(CEC2017)test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity,freedom from local optima,and solution accuracy.In addition,applying DMDS-HS to data clustering problems,the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms,which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

A NEW SYSTEM DYNAMIC EXTREMUM SELF-SEARCHING METHOD BASED ON CORRELATION ANALYSIS

Objective To propose a new dynamic extremum self searching method, which can be used in industrial processes extremum optimum control systems, to overcome the disadvantages of traditional method. Methods This algorithm is based on correlation analysis. A pseudo random binary signal m sequence u(t) is added as probe signal in system input, construct cross correlation function between system input and output, the next step hunting direction is judged by the differential sign. Results Compared with traditional algorithm such as step forward hunting method, the iterative efficient, hunting precision and anti interference ability of the correlation analysis method is obvious over the traditional algorithm. The computer simulation experimental given illustrate these viewpoints. Conclusion The correlation analysis method can settle the optimum state point of device operating process. It has the advantage of easy condition , simple calculate process.

Prime Box Parallel Search Algorithm: Searching Dynamic Dictionary in O(lg m) Time

Hashing and Trie tree data structures are among the preeminent data mining techniques considered for the ideal search. Hashing techniques have the amortized time complexity of O(1). Although in worst case, searching a hash table can take as much as θ(n) time [1]. On the other hand, Trie tree data structure is also well renowned data structure. The ideal lookup time for searching a string of length m in database of n strings using Trie data structure is O(m) [2]. In the present study, we have proposed a novel Prime Box parallel search algorithm for searching a string of length m in a dictionary of dynamically increasing size, with a worst case search time complexity of O(log2m). We have exploited parallel techniques over this novel algorithm to achieve this search time complexity. Also this prime Box search is independent of the total words present in the dictionary, which makes it more suitable for dynamic dictionaries with increasing size.

Improved Dynamic Q-Learning Algorithm to Solve the Lot-Streaming Flowshop Scheduling Problem with Equal-Size Sublots

The lot-streaming flowshop scheduling problem with equal-size sublots(ELFSP)is a significant extension of the classic flowshop scheduling problem,focusing on optimize makespan.In response,an improved dynamic Q-learning(IDQL)algorithm is proposed,tillizing makespan as feedback.To prevent blind search,a dynamic 8-greedy search strategy is introduced.Additionally,the Nawaz-Enscore-Ham(NEH)algorithm is employed to diversify solution sets,enhancing local optimality.Addressing the limitations of the dynamic 8-greedy strategy,the Glover operator complements local search efforts.Simulation experiments,comparing the IDQL algorithm with other itelligent algorithms,valldate its effectiveness.The performance of the IDQL algorithm surpasses that of its counterparts,as evidenced by the experimental analysis.Overall,the proposed approach offers a promising solution to the complex ELFSP,showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.

Collision-Free Path Planning with Kinematic Constraints in Urban Scenarios

In urban driving scenarios,owing to the presence of multiple static obstacles such as parked cars and roadblocks,planning a collision-free and smooth path remains a challenging problem.In addition,the path-planning problem is mostly non-convex,and contains multiple local minima.Therefore,a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban scenarios.The sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor,and the optimization-based method constructs a quadratic programming problem considering the desired state constraints,continuity constraints,driving corridor constraints,and kinematic constraints to perform path optimization.The experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.

Prediction of the lowest energy configuration for Lennard-Jones clusters

Based on the work of previous researchers, a new unbiased optimization algorithm—the dynamic lattice searching method with two-phase local search and interior operation (DLS-TPIO)—is proposed in this paper. This algorithm is applied to the optimization of Lennard-Jones (LJ) clusters with N=2–650, 660, and 665–680. For each case, the putative global minimum reported in the Cambridge Cluster Database (CCD) is successfully found. Furthermore, for LJ533 and LJ536, the potential energies obtained in this study are superior to the previous best results. In DLS-TPIO, a combination of the interior operation, two-phase local search method and dynamic lattice searching method is adopted. At the initial stage of the optimization, the interior operation reduces the energy of the cluster, and gradually makes the configuration ordered by moving some surface atoms with high potential energy to the interior of the cluster. Meanwhile, the two-phase local search method guides the search to the more promising region of the configuration space. In this way the success rate of the algorithm is significantly increased. At the final stage of the optimization, in order to decrease energy of the cluster further, the positions of surface atoms are further optimized by using the dynamic lattice searching method. In addition, a simple new method to identify the central atom of icosahedral configurations is also presented. DLS-TPIO has higher computing speed and success rates than some well-known unbiased optimization methods in the literature.