Hill-climbing Search Algorithm Research Articles

Enhancing Cryptographic Primitives through Dynamic Cost Function Optimization in Heuristic Search

The efficiency of heuristic search algorithms is a critical factor in the realm of cryptographic primitive construction, particularly in the generation of highly nonlinear bijective permutations, known as substitution boxes (S-boxes). The vast search space of 256! (256 factorial) permutations for 8-bit sequences poses a significant challenge in isolating S-boxes with optimal nonlinearity, a crucial property for enhancing the resilience of symmetric ciphers against cryptanalytic attacks. Existing approaches to this problem suffer from high computational costs and limited success rates, necessitating the development of more efficient and effective methods. This study introduces a novel approach that addresses these limitations by dynamically adjusting the cost function parameters within the hill-climbing heuristic search algorithm. By incorporating principles from dynamic programming, our methodology leverages feedback from previous iterations to adaptively refine the search trajectory, leading to a significant reduction in the number of iterations required to converge on optimal solutions. Through extensive comparative analyses with state-of-the-art techniques, we demonstrate that our approach achieves a remarkable 100% success rate in locating 8-bit bijective S-boxes with maximal nonlinearity, while requiring only 50,000 iterations on average—a substantial improvement over existing methods. The proposed dynamic parameter adaptation mechanism not only enhances the computational efficiency of the search process, but also showcases the potential for interdisciplinary collaboration between the fields of heuristic optimization and cryptography. The practical implications of our findings are significant, as the ability to efficiently generate highly nonlinear S-boxes directly contributes to the development of more secure and robust symmetric encryption systems. Furthermore, the dynamic parameter adaptation concept introduced in this study opens up new avenues for future research in the broader context of heuristic optimization and its applications across various domains.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Small Zoom Mismatch Adjustment Method for Dual-Band Fusion Imaging System Based on Edge-Gradient Normalized Mutual Information.

Currently, automatic optical zoom setups are being extensively explored for their applications in search, detection, recognition, and tracking. In visible and infrared fusion imaging systems with continuous zoom, dual-channel multi-sensor field-of-view matching control in the process of synchronous continuous zoom can be achieved by pre-calibration. However, mechanical and transmission errors of the zoom mechanism produce a small mismatch in the field of view after co-zooming, degrading the sharpness of the fusion image. Therefore, a dynamic small-mismatch detection method is necessary. This paper presents the use of edge-gradient normalized mutual information as an evaluation function of multi-sensor field-of-view matching similarity to guide the small zoom of the visible lens after continuous co-zoom and ultimately reduce the field-of-view mismatch. In addition, we demonstrate the use of the improved hill-climbing search algorithm for autozoom to obtain the maximum value of the evaluation function. Consequently, the results validate the correctness and effectiveness of the proposed method under small changes in the field of view. Therefore, this study is expected to contribute to the improvement of visible and infrared fusion imaging systems with continuous zoom, thereby enhancing the overall working of helicopter electro-optical pods, and early warning equipment.

Penerapan Algoritma Ascent Hill Climbing Pada Game Edukasi Penyusunan Deretan Angka Puzzle-8

Number preparation using Puzzle 8 is a game that is very popular with children at this time, this educational game has very broad benefits for improving memory and thinking patterns. The 8 Puzzle Game Algorithm must have a clear step-by-step process in order to achieve a goal in the Search system. Logic Game 8 puzzles using the Ascent Hill Climbing Algorithm are often used in artificial intelligence. The problem that occurs in the research, the lack of understanding of children to find a suitable algorithm to arrange the 8 Puzzle game according to the principle of how an algorithm works. The game of compiling 8 puzzles must follow the rules that have been determined in order to obtain the correct algorithm so as to produce an arrangement that is in accordance with the initial data. The benefit of this research is that it provides a solution to complete the game of 8 puzzles according to the rules of the Ascent Hill Climbing Search Algorithm so as to produce a solution in the search method. Based on the search process, the final result of the puzzle game is in the correct position of 8 squares according to the initial rule, so the search is stopped and produces a goal.

A multi-block clustering algorithm for high dimensional binarized sparse data

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods.

Making Hill-Climbing Great Again through Online Relaxation Refinement and Novelty Pruning

Delete relaxation is one of the most successful approaches to classical planning as heuristic search. The precision of these heuristics can be improved by taking some delete information into account, in particular through atomic conjunctions in the hCFF heuristic. It has recently been shown that this heuristic is especially effective when these conjunctions are learned online in a hill-climbing search algorithm. In this work, we devise a natural extension to this approach using novelty pruning, a recently-developed technique that prunes states based on whether they contain facts not seen before in the search. We evaluate our extension on the IPC benchmarks, where it beats LAMA, Mercury, and Dual-BFWS on many domains.

Novel technique for hill climbing search to reach maximum power point tracking

In this paper, a new technique has been proposed to solve the trade off common problem in hill climbing search algorithm (HCS) to reach maximum power point tracking (MPPT). The main aim of the new technique is to increase the power efficiency for the wind energy conversion system (WECS). The proposed technique has been combined the three-mode algorithm to be simpler. The novel algorithm is increasing the ability to reach the MPPT without delay. The novel algorithm shows fast tracking capability and enhanced stability under change wind speed conditions.

Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems

Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

Improved MPPT controls for a standalone PV/wind/battery hybrid energy system

In this paper, we present the modeling, optimization and control of a standalone hybrid energy system combining the photovoltaic and wind renewable energy sources to supply a dc electrical load, with storage batteries as a backup source. With the aim of improving the energy performance of the proposed system, we developed an MPPT controller based on new hybrid and robust approaches to evolve the power quality produced by both PV and Wind subsystems. For the PV subsystem, the proposed approach is based on the methods perturb and observe (P&O), sliding mode control (SMC) and fuzzy logic control (FLC). For the Wind subsystem, the proposed technique is based on the hill climbing search algorithm (HCS) and the fuzzy logic control. Also, to evaluate the efficiency of the developed controls and to analyze the behavior of each system during their maximum power point tracking, a comparison study was carried out with conventional techniques and the simulations are performed under varying weather conditions. The simulations results show the good performance of the proposed MPPT controls compared to other methods with better response time, a higher optimal power point and negligible oscillations.

Global search in single-solution-based metaheuristics

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

Maximizing Achievable Transmission Time in Cognitive Radio Networks Under Sensor-Aided Crowdsourced Spectrum Sensing

Abstract Spectrum-sensing as a service has been proposed and studied by many researchers over the past decade as a promising approach to support the viability of cognitive radio networks (CRNs). A spectrum-sensing service provider (SSP) provides information about spectrum occupancy to its clients that is generally more accurate than what clients can learn on their own. Two approaches are used by SSPs in their operation, the dedicated sensing infrastructure approach (sensor-aided CRN) and the crowdsensing approach. In this work, we assume a hybrid model where a dedicated sensing infrastructure is used along with crowdsensing. We study the tradeoff between sensing time paid by cognitive users to the SSP and their achievable transmission time. Our objective is to maximize the minimum achievable transmission time for any cognitive user in the network by carefully selecting the channels to be used. Two algorithms are proposed, one is based on the hill-climbing search algorithm (abbreviated HCA) and the other is a less optimal but faster greedy selection algorithm (abbreviated GSA). Results show that both HCA and GSA are within 3% of the optimal solution. Results also confirm that GSA is faster than HCA, while HCA outperforms GSA.

Matching for navigation map building for automated guided robot based on laser navigation without a reflector

PurposeLaser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.Design/methodology/approachFirst, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.FindingsThe experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.Originality/valueA linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

Adaptive Step Size Based Hill-Climb Search Algorithm for MPPT Control of DFIG-WECS With Reduced Power Fluctuation and Improved Tracking Performance

One of the major challenges in harnessing wind energy is to extract maximum power from intermittent generation of wind farms as wind power generation strongly depends on wind speed variation. Among different maximum power point tracking (MPPT) algorithms, traditionally, hill climb search (HCS) method is widely used because of its simple implementation and turbine parameter-independence. However, the conventional HCS algorithm has some drawbacks such as power fluctuation and speed-efficiency tradeoff. Similarly constant tip-speed ratio (TSR) MPPT control method requires precise knowledge of optimal TSR of the turbine. Therefore, in this paper a new adaptive step size based HCS controller is proposed to mitigate the deficiencies of other techniques by incorporating wind speed measurement. The function-based adaptive control scheme evaluates the step size by the variation of the wind speed and extracted power range. The proposed variable step size based HCS-MPPT system is simulated at variable wind speed. A prototype system is also built with a low-power doubly-fed induction generator (DFIG). It is found from the results that the proposed controller reduces the steady-state power fluctuation of the DFIG based wind energy conversion system significantly compared to the conventional HCS-MPPT controller and constant TSR based MPPT controller at variable wind speeds.

Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm

In designing a parallel hybrid electric vehicle, it is essential to select the optimal capacity of power sources and the optimal gear ratio of the torque coupler. The capacity of the power sources and the gear ratio of the torque coupler should be optimized simultaneously. However, since this process is excessively time-consuming, previous studies have selected the gear ratio of the torque coupler and then selected the capacity of power source. However, this approach cannot guarantee global optimization. In this paper, a feasible region is defined to satisfy the required performance of vehicle such as maximum speed, hill-climbing. and feasible points are selected inside the feasible region. In the conventional technique, the global optimal solution is obtained by simulating all feasible points. In the optimization technique, optimal points are simulated within the feasible region using several optimal search algorithms, such as the golden section search algorithm and the hillclimbing search algorithm. And using these algorithms, the number of simulations is reduced and the capacity of the power source and the gear ratio of the torque coupler are optimized simultaneously. Finally, the validity of the component sizing results is verified by comparing the global optimal solution obtained by applying the conventional technique with the solution obtained by applying the proposed optimization technique.

Magnus Wind Turbine Emulator With MPPT by Cylinder Rotation Control

An emulator for the nonconventional Magnus wind turbine was designed and developed in this study. A brief discussion is made of this special case of horizontal axis wind generator and of the main physics principles involving the Magnus phenomenon. A mathematical model was used to emulate the static behavior of the Magnus wind turbine and a detailed analysis is presented about its peculiar rotating cylinder characteristics. Based on the relationship between cylinder blade rotation and power coefficient, a hill climb search algorithm was developed to perform maximum power point tracking. The impact of the cylinder's rotation speed on the turbine net output power was evaluated. A controlled direct current motor was used to provide torque, based on the Magnus turbine model, and drive a permanent magnet synchronous generator (PMSG); the latter was controlled by a buck converter in order to extract the maximum generated power (MGP). Simulations of the Magnus wind turbine model and its maximum power point tracking (MPPT) control are also presented. A prototype of the proposed emulator was developed and operated by a user-friendly LabVIEW interface. Measurements of the power delivered to the load were acquired for different wind speeds; these results were analyzed and compared with simulated values showing a good behavior of the emulator with respect to the turbine model. The proposed control technique for maximizing the output power was validated by emulated results. The modeling and development of the Magnus turbine emulator also serve to encourage further studies on generation and control with this wind machine.

On defining a unique phylogenetic tree with homoplastic characters

This paper discusses the problem of whether creating a matrix with all the character state combinations that have a fixed number of steps (or extra steps) on a given tree T, produces the same tree T when analyzed with maximum parsimony or maximum likelihood. Exhaustive enumeration of cases up to 20 taxa for binary characters, and up to 12 taxa for 4-state characters, shows that the same tree is recovered (as unique most likely or most parsimonious tree) as long as the number of extra steps is within 1/4 of the number of taxa. This dependence, 1/4 of the number of taxa, is discussed with a general argumentation, in terms of the spread of the character changes on the tree used to select character state distributions. The present finding allows creating matrices which have as much homoplasy as possible for the most parsimonious or likely tree to be predictable, and examination of these matrices with hill-climbing search algorithms provides additional evidence on the (lack of a) necessary relationship between homoplasy and the ability of search methods to find optimal trees.

Establishment of ionospheric model containing sporadic E and its applications in target height measurement

Ionosperic sporadic-E layer (Es layer) is the irregular structure in ionosphere which often occurs in summer of China, but the current model of height estimation with high frequency rays does not consider the Es layer, which often makes a large error in the estimation of the target height. In this paper, the parameters of the actual ionosphere are analyzed by using the measured data of the ionospheric vertical measurement station and the information about the variation of the ionosphere in southeastern China which was obtained in recent years. The measured data indicate that the probability of occurrence of Es in China is relatively high, especially in summer. When Es appears in summer, the probability of its cut-off frequency greater than 4.5 MHz reaches up to 83.6%, therefore, it is necessary to study the target height measurement model and algorithm when the ionosphere contains Es. Firstly, on the basis of the quasi-parabolic segments ionosphere model and real ionosphere parameters, the ionosphere model containing the Es layer is established. In this model, Es layer and its connection layer with the E layer are represented by parabola and reverse parabola respectively. Then, the high frequency transmission characteristics of the target micro multipath are analyzed based on Es model. The simulation shows that 4 multipath echoes can be simulated by the characteristics of different slant ranges and Doppler frequencies in the multiple echoes of the target. By matching the simulated 4 multipath echoes with the actual high frequency echo of the target, when the matching degree reaches a maximum value, the estimated height value can be obtained. Finally, based on the micro multipath difference between high frequency rays and the ionospheric model with Es layer, a height estimation method using matched-field processing and hill climbing search algorithm is proposed. This method can greatly reduce the search time for obtaining the real height value. Through theoretical analysis and experimental verification, the relationships between the ionospheric plasma frequency and height, between the transmission path of high frequency rays and the elevation angle/transmitting frequency, and between the micro path characteristics of high frequency rays and the height of target are obtained. Ionospheric model with the Es layer and the new target height measurement method based on the matched-field processing can accurately estimate the height of the target and have a faster calculation speed.

A 0.45-to-1.2-V Fully Digital Low-Dropout Voltage Regulator With Fast-Transient Controller for Near/Subthreshold Circuits

A low quiescent current digital low-dropout (D-LDO) voltage regulator with fast-transient response time is proposed for self-powered wireless sensor applications operating at near/subthreshold supply voltage. The D-LDO regulator incorporates both hill-climbing and binary search algorithms (HCBS) in the control logic, thus leveraging on each other's strengths to minimize the output voltage's ripple and the quiescent current during the steady-state period as well as output voltage's spike and response time during the transition period. Additional features such as hysteresis mode control and freeze mode control are incorporated into the system to improve the performance of the D-LDO regulator. A dynamic comparator is proposed for the near/subthreshold supply voltage operation, which minimizes the voltage error and improves the maximum operating frequency. Fabricated in 130-nm CMOS technology, the D-LDO regulator regulates the output voltage ${V}_{{\rm OUT}}$ from 350 to 1150 mV, while the input supply voltage ${V}_{\text{IN}}$ ranges from 450 to 1200 mV. At a ${V}_{{\rm OUT}}$ of 450 mV, ${V}_{{\rm IN}}$ of 500 mV and an operating frequency of 10 MHz, the regulator delivers 1500- ${\mu }$ A load current with $I_{\text{QUIESCENT}}$ of 8.9 ${\mu }$ A and a transient response time of 1.6 ${\mu }$ s. The maximum current and power efficiencies reach 99.9% and 89.9%, respectively. The measured line regulation and load regulation are 1.6 and 0.6 mV/mA, respectively.

Maximum Power Point Tracking of Hybrid Tidal Current—Supercapacitor Energy Conversion System

This paper proposes control of maximum power tracking system of tidal current energy system. A permanent magnet synchronous generator (PMSG) works as a variable speed generator in the proposed energy system. A controller was applied to achieve the maximum power control of tidal current turbine on a wide range of water current speed change. A dynamic model and simulation of the energy system coupled with current change are presented. The measured DC voltage and DC current are used to determine the position of maximum power point that controls the DC/DC boost converter duty cycle depending on the Hill Climb Search (HCS) algorithm. This algorithm doesn’t require any information or measurements about current’s speed change or generator’s characteristics. A supercapacitor added to fix the load voltage despite of tidal current speed or load variations. Simulation results show the effectiveness of the controller proposed system.

Heuristics as Markov chains

Comparing heuristics has been a major issue from the early days of heuristic search. From the very beginning, measures of the accuracy of heuristic functions were strongly based on the number of nodes generated, and they are often still based on it. In this work, an alternative approach is suggested: to model the accuracy of admissible heuristic functions as the probability of the heuristic hill-climbing search algorithm to find the goal state in a number of steps that equals the heuristic value of an arbitrary node. The resulting method serves to assess on the accuracy of both consistent and inconsistent heuristic functions. Comparisons with different sizes of the sliding-tile puzzle show that the model suggested predicts the accuracy of the heuristic function with a good precision. The resulting procedure is used to derive figures on the accuracy of a large number of heuristics for the 15-Puzzle with different variants of the search algorithm IDA?.