Space Search Algorithm Research Articles

Slime mould algorithm with mechanism of leadership and self-phagocytosis for multilevel thresholding of color image

Threshold based segmentation method is widely used because of its simplicity, high efficiency and easy implementation. However, as the number of thresholds increases, the algorithm's search space also increases, increasing computational complexity, which reduces computational efficiency and segmentation accuracy. Additionally, the existing multi-threshold (MT) segmentation methods still struggle with slow convergence speed and low solution accuracy. The optimum threshold for the image is determined in this study using the Slime Mould Algorithm with the Mechanism of Leadership and Self-phagocytosis (SMA-MLS). Firstly, the initial population production method based on Logistic-Tent mapping is introduced to enhance the diversity of algorithm solutions. Secondly, the position update formula with leadership mechanism is proposed to improve the convergence speed and accuracy. In addition, the adaptive combined mutation mechanism balances the exploration and exploitation ability. Finally, the self-phagocytosis mechanism maintains population diversity and heritability. A series of benchmark test suites are employed to evaluate the performance of the algorithm. Experimental results demonstrate that SMA-MLS does show good performance. It is superior in terms of convergence accuracy, convergence speed and stability. In the MT segmentation experiments, by using Kapur as the objective function and working with color images, SMA-MLS proved to be effective in the MT segmentation problem of images. Moreover, SMA-MLS will be applied to mechanical parameter optimization, neural network and other fields in future research.

An improved near-field weighted subspace fitting algorithm based on niche particle swarm optimization for ultrasonic guided wave multi-damage localization

The ultrasonic guided wave-based method for multi-damage localization has been widely proposed. However, the precision of this method is directly correlated with both the quantity of sensors employed and the intricacy of the implementation process. This relationship poses a challenge in striking a balance between the accuracy and efficiency. To improve the computational efficiency under the premise of ensuring the accuracy of multi-damage localization, this paper proposes a near-field weighted subspace fitting algorithm based on niche-particle swarm optimization. Firstly, the fitting relationship between the signal subspace of the diffraction wave and the array steering vector is established under the uniform linear array. Secondly, a multi-dimensional solution space search algorithm based on niche-particle swarm optimization is proposed to improve the search efficiency of damage. Finally, the algorithm is verified by performance comparison, finite element simulation and experiment. The results show that compared with the same type of method, the algorithm improves the computational efficiency by nearly threefold under the identifiable multi-damage conditions. Additionally, the angle error is 1 ∼ 6°, and the distance error is 1 ∼ 20 mm.

Preprocessing to reduce the search space: Antler structures for feedback vertex set

The goal of this paper is to open up a new research direction aimed at understanding the power of preprocessing in speeding up algorithms that solve NP-hard problems exactly. We explore this direction for the classic Feedback Vertex Set problem on undirected graphs, leading to a new type of graph structure called antler decomposition, which identifies vertices that belong to an optimal solution. It is an analogue of the celebrated crown decomposition which has been used for Vertex Cover. We develop the graph structure theory around such decompositions and develop fixed-parameter tractable algorithms to find them, parameterized by the number of vertices for which they witness presence in an optimal solution. This reduces the search space of fixed-parameter tractable algorithms parameterized by the solution size that solve Feedback Vertex Set.

Learning to project in a criterion space search algorithm: an application to multi-objective binary linear programming

Learning to project in a criterion space search algorithm: an application to multi-objective binary linear programming

Search algorithms in state space for solving artificial intelligence problems

A comprehensive analysis of state space search algorithms widely used in artificial intelligence (AI) problems has been carried out. Various classes of algorithms: blind exhaustive and limited search methods, heuristic algorithms, metaheuristic approaches, as well as their hybrid combinations are considered. A comparison was made based on key criteria: search strategy, systematicity, optimality, computational complexity, scalability, parallelizability, etc. Areas of effective application of each type of algorithm are identified. Recommendations for choosing an appropriate strategy depending on the characteristics of the problem are formulated. Promising directions for the development of methods based on hybridization, as well as machine learning technologies for adaptive optimization of search parameters, are considered. Keywords artificial intelligence, state space, search algorithms, blind search, heuristic search, metaheuristics, hybrid algorithm, machine learning

An Efficient Dynamic Programming Algorithm for Finding Group Steiner Trees in Temporal Graphs

The computation of a group Steiner tree (GST) in various types of graph networks, such as social network and transportation network, is a fundamental graph problem in graphs, with important applications. In these graphs, time is a common and necessary dimension, for example, time information in social network can be the time when a user sends a message to another user. Graphs with time information can be called temporal graphs. However, few studies have been conducted on GST in terms of temporal graphs. This study analyzes the computation of GST for temporal graphs, i.e., the computation of temporal GST (TGST), which is shown to be an NP-hard problem. We propose an efficient solution based on a dynamic programming algorithm for our problem. This study adopts new optimization techniques, including graph simplification, state pruning, and A ∗ search, are adopted to dramatically reduce the algorithm search space. Moreover, we consider three extensions for our problem, namely the TGST with unspecified tree root, the progressive search of TGST, and the top-N search of TGST. Results of the experimental study performed on real temporal networks verify the efficiency and effectiveness of our algorithms.

Five-Level Inverter Fed Sensorless PMSM Drive Using Modified MPTC with Novel Reduced Search Space Algorithm

Five-Level Inverter Fed Sensorless PMSM Drive Using Modified MPTC with Novel Reduced Search Space Algorithm

Exploring spaces of semi-directed level-1 networks

Semi-directed phylogenetic networks have recently emerged as a class of phylogenetic networks sitting between rooted (directed) and unrooted (undirected) phylogenetic networks as they contain both directed as well as undirected edges. While various spaces of rooted phylogenetic networks and unrooted phylogenetic networks have been analyzed in recent years and several rearrangement moves to traverse these spaces have been introduced, little is known about spaces of semi-directed phylogenetic networks. Here, we propose a simple rearrangement move for semi-directed phylogenetic networks, called cut edge transfer (CET), and show that the space of semi-directed level-1 networks with precisely k reticulations is connected under CET. This level-1 space is currently the predominantly used search space for most algorithms that reconstruct semi-directed phylogenetic networks. Our results imply that every semi-directed level-1 network with a fixed number of reticulations and leaf set can be reached from any other such network by a sequence of CETs. By introducing two additional moves, R^+ and R^-, that allow for the addition and deletion, respectively, of a reticulation, we then establish connectedness for the space of all semi-directed level-1 networks on a fixed leaf set. As a byproduct of our results for semi-directed phylogenetic networks, we also show that the space of rooted level-1 networks with a fixed number of reticulations and leaf set is connected under CET, when translated into the rooted setting.

Generating balanced workload allocations in hospitals

As pressure on healthcare systems continues to increase, it is becoming more and more important for hospitals to properly manage the high workload levels of their staff. Ensuring a balanced workload allocation between various groups of employees in a hospital has been shown to contribute considerably towards creating sustainable working conditions. However, allocating work to different organizational units in a fair manner is not straightforward when it involves complex decision-making processes. In this paper we set out to balance the workload of heterogeneous hospital wards by optimizing the patient admission scheduling problem. Given the multi-period nature of patient admission scheduling, we introduce a new equity objective that captures both spatial (between hospital wards) and temporal (between days in the planning period) workload balancing. The resulting bi-objective problem is solved using an exact criterion space search algorithm. Our computational study employs problem instances that have been generated based on real-world data. The results demonstrate how spatially and temporally balanced workload allocations can be generated by minimizing the proposed equity objective. Moreover, we analyze sets of nondominated solutions to gain various insights into the trade-off between schedule cost and workload balance.

A global-local hybrid strategy with adaptive space reduction search method for structural health monitoring

The difficulty in computational convergence poses challenges of application for traditional heuristic optimization algorithms to solve the optimization-based structural identification problem, especially for the large-scale and complex structural systems where considerable number of unknown parameters and degrees of freedom involved. Unlike the classic identification methods, in this paper, a novel hybrid strategy, coarsely exploring the relatively large search limits with the improved Jaya algorithm and adaptive search space reduction method in the global stage, and then fine-tuning the identified best solution with local optimization methods to the optimum in the local stage, is proposed and evaluated. The improved Jaya algorithm includes three improvements compared to its original version, fuzzy clustering competitive learning, experience learning and Cauchy mutation mechanisms. Gradient based Levenberg-Marquardt method, sequential quadratic programming method and non-gradient based Nelder-Mead simplex method are inserted as local mathematical optimizers to further enhance identification accuracy and efficiency. The superiority of proposed improved Jaya algorithm is validated in optimizing classical and CEC05 benchmark functions by comparing with several state-of-the-art algorithms. Furthermore, the effectiveness of proposed global-local hybrid method is verified by a numerical example of truss structure and an experimental test of the steel grid benchmark structure with incomplete set of noise-polluted measurements. The statistical results show that the improved Jaya algorithm and adaptive search space reduction method combined with sequential quadratic programming can achieve better performance in structural damage identification than other methods.

A generality analysis of multiobjective hyper-heuristics

Selection hyper-heuristics have emerged as high level general-purpose search methodologies that mix and control a set of low-level (meta) heuristics. Previous empirical studies over a range of single objective optimisation problems have shown that the number and type of low-level (meta) heuristics used are influential to the performance of selection hyper-heuristics. In addition, move acceptance strategies play an important role and can significantly affect the overall performance of a hyper-heuristic. In this paper, we introduce an adapted variant of an existing learning automata based multiobjective hyper-heuristic from the literature. We investigate the performance and generality level of the proposed method, and another learning automata based selection hyper-heuristic, operating over a search space of multiobjective evolutionary algorithms (MOEAs) across two well-known multiobjective optimisation benchmarks. The experimental results demonstrate that, regardless of the number and type of low-level metaheuristics available, the learning automata based hyper-heuristics outperform each constituent MOEA individually, and an online learning and random choice selection hyper-heuristic from the literature. This performance and generality is shown to be consistent across a number of different move acceptance strategies.

Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text.

Though several AI-based models have been established for COVID-19 diagnosis, the machine-based diagnostic gap is still ongoing, making further efforts to combat this epidemic imperative. So, we tried to create a new feature selection (FS) method because of the persistent need for a reliable system to choose features and to develop a model to predict the COVID-19 virus from clinical texts. This study employs a newly developed methodology inspired by the flamingo's behavior to find a near-ideal feature subset for accurate diagnosis of COVID-19 patients. The best features are selected using a two-stage. In the first stage, we implemented a term weighting technique, which that is RTF-C-IEF, to quantify the significance of the features extracted. The second stage involves using a newly developed feature selection approach called the improved binary flamingo search algorithm (IBFSA), which chooses the most important and relevant features for COVID-19 patients. The proposed multi-strategy improvement process is at the heart of this study to improve the search algorithm. The primary objective is to broaden the algorithm's capabilities by increasing diversity and support exploring the algorithm search space. Additionally, a binary mechanism was used to improve the performance of traditional FSA to make it appropriate for binary FS issues. Two datasets, totaling 3053 and 1446 cases, were used to evaluate the suggested model based on the Support Vector Machine (SVM) and other classifiers. The results showed that IBFSA has the best performance compared to numerous previous swarm algorithms. It was noted, that the number of feature subsets that were chosen was also drastically reduced by 88% and obtained the best global optimal features.

改进蚁群优化算法的最大似然DOA估计方法

In order to alleviate the huge amount of calculation when using the continuous domain ant colony optimization (ACO) algorithm to handle the maximum likelihood (ML) estimation problem, we propose a maximum likelihood direction of arrival (ML-DOA) estimation method based on modified ant colony optimization (MACO) algorithm. Firstly, MACO algorithm adopts the elite reverse learning strategy to obtain a better initial solution group. Secondly, the optimization method of ant colony is conducted by combining global cross-neighborhood search and local search of Gaussian kernel function to expand the algorithm search space and accelerate the convergence speed. Finally, the nonlinear global optimal solution of ML estimation method is obtained. Simulation results show that, compared with ML estimation methods based on particle swarm optimization (PSO) algorithm and ACO algorithm, the convergence speed of the ML-MACO algorithm is 4 times faster than that of the ML-ACO algorithm, the computational load is 1/3 that of the ML-ACO algorithm, the resolution success rate is higher than the ML-PSO algorithm and the ML-ACO algorithm, and the estimation error is less than the ML-PSO algorithm and the ML-ACO algorithm. The ML-MACO algorithm maintains the excellent estimation performance of the ML algorithm with lower computational effort, better convergence performance, and higher estimation accuracy.

An exact criterion space search method for a bi-objective nursing home location and allocation problem

A health care system will be survived if it is economically affordable and elderly people can receive its services in case of availability. Hence, a network should be designed for home nursing of elderly people considering both economic and well-fare objectives. Finding optimal solutions to both mentioned objectives in health care systems, especially for elderly people, is challenging nowadays. Hence, it is necessary to find near-optimal solutions in action. This paper presents a new criterion space search algorithm for achieving all non-dominated points in a bi-objective nursing home location–allocation problem. Concepts of Box and Balanced Box methods and modified e-constraint are used in designing the proposed algorithm. The main focus of the research is to decrease potential areas that could involve non-dominated points by defining Minimum Feasible Rectangles during the search procedure. The performance of the proposed algorithm is analyzed by using two real data sets taken from the literature in the field of nursing home location–allocation problems. The proposed algorithm can outperform previous recently developed exact methods based on obtained results, especially from a computational time viewpoint.

UAV Formation Transformation Based on Two-Stage Path Planning Method

With the maturity of unmanned aerial vehicle (UAV) technology, the application of UAV in various fields is more in-depth. A method to quickly and accurately solve the flight paths of a large number of UAVs from one formation to another is crucial for application scenarios that require UAVs to perform formation transformation tasks. In this paper, the path planning of UAV swarm formation transformation is divided into two stages: rough planning and fine planning. In the rough planning stage, a 0-1 variable planning model is established to match the start and end points of the UAV flight path, and the model is solved using a Strengthen Elitist GA Algorithm to quickly obtain high-quality feasible solutions. In the fine planning stage, four models including behavioral mode, obstacle avoidance criterion, optimal waypoint selection and target exchange are established. On this basis, a heuristic fixed-step space search algorithm is designed and implemented to plan the path finely, and the path planning for the UAV formation transformation in two scenarios is finally completed.

Practical Aspects of Membership Problem of Watson-Crick Context-free Grammars

This paper focuses on Watson-Crick languages inspired by DNA computing, their models, and algorithms for deciding the language membership. It analyzes a recently introduced algorithm called WK-CYK and introduces a state space search algorithm that is based on regular Breadth-first search but uses a number of optimizations and heuristics to be efficient in practical use and able to analyze longer inputs. The key parts are the heuristics for pruning the state space (detecting dead ends) and heuristics for choosing the most promising branches to continue the search. These two algorithms have been tested with 20 different Watson-Crick grammars (40 including their Chomsky normal form versions). While WK-CYK is able to decide the language membership in a reasonable time for inputs of the length of roughly 30-50 symbols and its performance is very consistent for all kinds of grammars and inputs, the state space search is usually (89-98 % of cases) more efficient and able to do the computation for inputs with lengths of hundreds or even thousands of symbols. Thus, the state space search has the potential to be a good tool for practical Watson-Crick membership testing and is a good basis for improvement the efficiency of the algorithm in the future.

Bi‐objective optimization of the tactical allocation of job types to machines: mathematical modeling, theoretical analysis, and numerical tests

AbstractWe introduce a tactical resource allocation model for a large aerospace engine system manufacturer aimed at long‐term production planning. Our model identifies the routings a product takes through the factory, and which machines should be qualified for a balanced resource loading, to reduce product lead times. We prove some important mathematical properties of the model that are used to develop a heuristic providing a good initial feasible solution. We propose a tailored approach for our class of problems combining two well‐known criterion space search algorithms, the bi‐directional ε‐constraint method and the augmented weighted Tchebycheff method. A computational investigation comparing solution times for several solution methods is presented for 60 numerical instances.

Situated Grid Pathfinding Among Moving Obstacles (Extended Abstract)

There has been much recent work on finding paths in grid maps among moving obstacles. However, in addition to assuming complete omniscience regarding the map and the obstacles' trajectories, previous work has also assumed that time stands still while the agent plans. In this paper, we address situated pathfinding, in which time passes and the obstacles continue to move while the agent plans. We study the choice of state space representation and search algorithm, with a focus on whether conclusions drawn from studies in the off-line case continue to hold in a situated setting.

A Study on the Parameter Selection of Bat Algorithm in in Optimizing Parameters in Camera Auto Calibration Problem

In camera auto calibration, the major goal is to discover intrinsic parameter values that minimize the cost function. This study proposes to implement Bat algorithm, a stochastic optimization technique, to determine the optimum intrinsic parameter values. Each bat in the Bat Algorithm represents a potential solution to the issue, and each dimension in the Bat Algorithm's search space represents one of the basic parameters: skew, focal length, and magnification factor. The Kruppa's equation is the basis for the cost function in this study. By studying the echolocation behavior of the microbats, the bats will try to improve the fitness with each iteration. The Bat Algorithm's performance is evaluated using a case study from a database from Le2i Universite de Bourgoune. This paper studies the correlation of different parameters selection in Bat Algorithm in solving the camera auto-calibration problem. Finding shows that Bat Algorithm produces output that as expected as theory of Computational Intelligence suggested.

Adaptive simulated annealing with greedy search for the circle bin packing problem

We introduce a new bin packing problem, termed the circle bin packing problem with circular items (CBPP-CI). The problem involves packing all the circular items into multiple identical circle bins as compact as possible with the objective of minimizing the number of used bins. We first define the tangent occupying action (TOA) and propose a constructive greedy algorithm that sequentially packs the items into places tangent to the packed items or the bin boundaries. Moreover, to avoid falling into a local minimum trap and efficiently judge whether an optimal solution has been established, we continue to present an adaptive simulated annealing with greedy search (ASA-GS) algorithm that explores and exploits the search space efficiently. Specifically, we offer two novel local perturbation strategies to jump out of the local optimum and incorporate the greedy search to achieve faster convergence. The parameters of ASA-GS are adaptive according to the number of items so that they can be size-agnostic across the problem scale. We design two sets of new benchmark instances, and the empirical results show that ASA-GS completely outperforms the constructive greedy algorithm. Moreover, for the problem of packing unequal circles to a fixed size circular container to maximize the area of circles packed, ASA-GS outperforms the advanced formulation space search (FSS) algorithm in terms of solution quality and computational cost, inferring that our approach is not only adapted to CBPP-CI but also works well when the number of bins is reduced to one to become a typical circle packing problem.