Fractal image encoding based on adaptive search

Finding the optimal algorithm between an efficient encoding process and therate distortion is the main research in fractal image compression theory. A new method has beenproposed based on the optimization of the Least-Square Error and the orthogonal projection. A largenumber of domain blocks can be eliminated in order to speed-up fractal image compression. Moreover,since the rate-distortion performance of most fractal image coders is not satisfactory, an efficientbit allocation algorithm to improve the rate distortion is also proposed. The implementation andcomparison have been done with the feature extraction method to prove the efficiency of the proposedmethod.

An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider interest in solving optimization problems.However,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN deteriorate.This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN.Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms.Unlike the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation characteristics.In addition,the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality,fitness,and convergence speed of the original RUN.Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original RUN.To promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness.Simulation results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics.Therefore,with its higher computation efficiency,Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.

Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm

Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.

Adaptive backtracking search optimization algorithm with pattern search for numerical optimization

The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles(UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately,this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learningbased heuristic, namely, population based adaptive large neighborhood search(P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size(20 targets)instances in much shorter time.

Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .

A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’profiles.Then,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious.The IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental evaluations.Normal user access behavior in relation to the database is modelled using the data set.The false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other methods.The experimental results indicate that our proposed method results in very small FN and FP rates.

Hybrid Clustering Algorithms with GRASP to Construct an Initial Solution for the MVPPDP

Mobile commerce(m-commerce)contributes to increasing the popularity of electronic commerce(e-commerce),allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time.As demand for e-commerce increases tremendously,the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction.One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem(MVPPDP),where a selected set of pickup and delivery customers need to be served within certain allowed trip time.In this paper,we proposed hybrid clustering algorithms with the greedy randomised adaptive search procedure(GRASP)to construct an initial solution for the MVPPDP.Our approaches first cluster the search space in order to reduce its dimensionality,then use GRASP to build routes for each cluster.We compared our results with state-of-the-art construction heuristics that have been used to construct initial solutions to this problem.Experimental results show that our proposed algorithms contribute to achieving excellent performance in terms of both quality of solutions and processing time.

A GRASP Algorithm for Multi-objective Circuit Partitioning

Circuit partitioning plays a crucial role in very large-scale integrated circuit (VLSI) physical design automation. With current trends, partitioning with multiple objectives which includes cutsize, area, delay, and power obtains much concentration. In this paper, a multi-objective greedy randomized adaptive search procedure (GRASP) is presented for simultaneous cutsize and circuit delay minimization. Each objective is assigned a preference or weight to direct the search procedure and generate a variety of efficient solutions by changing the preference. To get a good initial partition with minimal cutsize and circuit delay, the gain of each module in a circuit is computed by considering both signal nets and circuit delay. The performance of the proposed algorithm is evaluated on a standard set of partitioning benchmark. The experimental results show that the proposed algorithm can generate a set of Pareto optimal solutions and is efficient for tackling multi-objective circuit partitioning.

VLSI implementation of MIMO detection for 802.11n using a novel adaptive tree search algorithm

A 4×4 64-QAM multiple-input multiple-output （MIMO） detector is presented for the application of an IEEE 802.1 In wireless local area network. The detector is the implementation of a novel adaptive tree search （ATS） algorithm, and multiple ATS cores need to be instantiated to achieve the wideband requirement in the 802.11 n standard. Both the ATS algorithm and the architectural considerations are explained. The latency of the detector is 0.75 μs, and the detector has a gate count of 848 k with a total of 19 parallel ATS cores. Each ATS core runs at 67 MHz. Measurement results show that compared with the floating-point ATS algorithm, the fixed-point imple- mentation achieves a loss of 0.9 dB at a BER of 10^-3.

A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images

Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.

An adaptive fast search algorithm for block motion estimation in H.264

Motion estimation is an important issue in H.264 video coding systems because it occupies a large amount of encoding time.In this paper,a novel search algorithm which utilizes an adaptive hexagon and small diamond search (AHSDS) is proposed to enhance search speed.The search pattern is chosen according to the motion strength of the current block.When the block is in active motion,the hexagon search provides an efficient search means;when the block is inactive,the small diamond search is adopted.Simulation results showed that our approach can speed up the search process with little effect on distortion performance compared with other adaptive approaches.

Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

Enhanced elastic beam model with BADS integrated for settlement assessment of immersed tunnels

Excessive settlement may induce structural damage and water leakage in immersed tunnels,seriously threatening the tunnels’safety.However,making accurate assessment of the settlement in immersed tunnels is difficult due to the incomplete knowledge of the geotechnical parameters and the inadequacy of the model itself.This paper proposes an effective method to accurately assess the settlement in immersed tunnels.An enhanced beam on elastic foundation model(E-BEFM)is developed for the settlement assessment,with the Bayesian adaptive direct search algorithm adopted to estimate unknown model parameters based on previous observations.The proposed method is applied to a field case of the Hong Kong–Zhuhai–Macao immersed tunnel.The original BEFM is used for comparison to highlight the better assessment performance of E-BEFM,particularly for joints’differential settlement.Results show that the proposed method can provide accurate predictions of the total settlement,angular distortion(a representation of tubes’relatively differential settlement),and joints’differential settlement,which consequently supports the associated maintenance decision-making and potential risk prevention for immersed tunnels in service.

Solving vehicle routing problem with time windows using metaheuristic approaches

Purpose-Drugs are strategic products with essential functions in human health.An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health.The vehicle-routing problem,focused on finding the lowest-cost routes with available vehicles and constraints,such as time constraints and road length,is an important aspect of this.In this paper,the vehicle routing problem(VRP)for a pharmaceutical company in Turkey is discussed.Design/methodology/approach-A mixed-integer programming(MIP)model based on the vehicle routing problem with time windows(VRPTW)is presented,aiming to minimize the total route cost with certain constraints.As the model provides an optimum solution for small problem sizes with the GUROBI®solver,for large problem sizes,metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed.A real dataset was used to analyze the effectiveness of the metaheuristic algorithms.The proposed simulated annealing(SA)and adaptive large neighborhood search(ALNS)were evaluated and compared against GUROBI®and each other through a set of real problem instances.Findings-The model is solved optimally for a small-sized dataset with exact algorithms;for solving a larger dataset,however,metaheuristic algorithms require significantly lesser time.For the problem addressed in this study,while the metaheuristic algorithms obtained the optimum solution in less than one minute,the solution in the GUROBI®solver was limited to one hour and three hours,and no solution could be obtained in this time interval.Originality/value-The VRPTW problem presented in this paper is a real-life problem.The vehicle fleet owned by the factory cannot be transported between certain suppliers,which complicates the solution of the problem.

Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution tasks.The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW.Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce.However,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual operation.Hence,we propose the robust optimization model based on a route-related uncertain set to tackle this problem.Moreover,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem.The effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

Free-floating bike-sharing green relocation problem considering greenhouse gas emissions

This paper introduces the problem of green bike relocation considering greenhouse gas emissions in free-floating bike-sharing systems(FFBSSs)and establishes a mathematical model of the problem.This model minimizes the total imbalance degree of bikes in the FFBSS and the greenhouse gas emissions generated by relocation in the FFBSS.Before the relocation phase,the FFBSS is divided into multiple relocation areas using a two-layer clustering method to reduce the scale of the relocation problem.In the relocation phase,the relocation route problem is converted into a pickup and delivery vehicle-routing problem.Then,an adaptive variable neighbourhood tabu search algorithm with a three-dimensional tabu list is proposed,which can simultaneously solve the relocation problem and the routing problem.A computational study based on the actual FFBSS used in Shanghai shows that this method can effectively solve the green relocation problem of FFBSSs.