Roulette Wheel Selection Mechanism Research Articles

Enhancing wireless sensor network security and efficiency with CNN-FL and NGO optimization

In this study, we discuss how Wireless Sensor Networks (WSN) are susceptible to malicious assaults from malfunctioning nodes, which jeopardise the security of data transmission in crucial applications. We use a Convolutional Neural Network coupled with Fuzzy Logic (CNN-FL) for improved deep learning, introducing a unique method, effectively identifying and categorizing trustworthy and malicious nodes. This is combined with a routing strategy enhanced by the Neuro Genetic Optimizer (NGO), built on the LEACH routing protocol and based on a Roulette wheel selection mechanism. The WSN's lifetime is increased by our suggested routing method, which not only provides safe data transmission but also dramatically reduces latency and energy consumption. Simulation findings show that our technique outperforms current protocols as ASNGSRA, DMCNN, and FRCSROD in terms of packet delivery ratio, energy economy, and latency analysis.

A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP

In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP-ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP-ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed-up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system-roulette wheel method-bitmask tabu (MMAS-RWM-BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed-up ratio and less runtime, proving that the proposed FP-ACO is more suitable for solving TSP.

MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p (Delta mathrm{P}). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution

The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios.

Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm

A power system’s operating cost needs to be minimized by satisfying varying load demand while taking into account the prevailing constraints in a multiple unit electrical power system. In this study, by using genetic algorithms (GA), a short-term thermal unit commitment problem was solved and an economical generating unit schedule was made with the solution obtained. Taking into account the negative effects of emissions due to the use of fossil fuels, emission costs were added to the objective function together with fuel and start-up costs. The GA chromosome structure was formed by binary encoding, new generations were selected by roulette wheel selection mechanism and single point crossover was applied. The representation, formulation and the simulation results of the problem for a 5-unit test system during the scheduling hours of the period are presented. The number and the operating hours of the generating units to be committed were determined by satisfying the prevailing constraints. During the planning period, 13360 MW of power demand was met by 755 MW of spinning reserve. Total operating cost was calculated as $430330. Of the total operating cost, 32% consists of emission costs.

Virtual Machine Consolidation using Roulette wheel selection Strategy

Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. Virtual Machine [VM] consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/under load detection, VM selection and VM placement. In Our Proposed Model We are going to use Roulette-Wheel Selection Strategy, Where the VM selects the Instance type and Physical Machine [PM] using Roulette-Wheel Selection Mechanism Keywords—searchable encryption, dynamic update, cloud computing

Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification

<span>Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, </span><span>whereas exploration aims towards new promising regions during a search process. </span><span>The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. </span><span>Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and </span><span>computational time. </span><span>The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.</span>

The quasispecies regime for the simple genetic algorithm with roulette wheel selection

Abstract We introduce a new parameter to discuss the behavior of a genetic algorithm. This parameter is the mean number of exact copies of the best-fit chromosomes from one generation to the next. We believe that the genetic algorithm operates best when this parameter is slightly larger than 1 and we prove two results supporting this belief. We consider the case of the simple genetic algorithm with the roulette wheel selection mechanism. We denote by ℓ the length of the chromosomes, m the population size, pC the crossover probability, and pM the mutation probability. Our results suggest that the mutation and crossover probabilities should be tuned so that, at each generation, the maximal fitness multiplied by (1 - pC)(1 - pM)ℓ is greater than the mean fitness.

A modified artificial bee colony algorithm based on converge-onlookers approach for global optimization

Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. It has received significant interest from researchers studying in different fields since it was invented. However, there are still some problems in ABC algorithm, such as slow to converge, easily get trapped in local optima and hard to find the best solution from all feasible solutions, and so on. In this work, we propose a modified algorithm called converge-onlookers ABC (COABC) algorithm. In order to improve the exploitation, a new solution search equation in the onlooker stage by applying the best solution of the previous iteration to guide the search of new candidate solutions was presented. In COABC algorithm, the roulette wheel selection mechanism based on nectar amounts of each food source was saved. Moreover, all employed bees to update not only once in each iteration, which helps improving both solution quality and convergence rate of ABC algorithm remarkably. The experimental results tested on numerical benchmark functions show that the COABC algorithm has excellent solution quality and convergence characteristics comparing to basic ABC algorithm and performs better than the state-of-art algorithm in some problems. The optimization result in solving the practical problem demonstrates the COABC is a prospect method and can be used in the engineering field.

A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

Energy Efficient Wireless Communication Using Genetic Algorithm Guided Faster Light Weight Digital Signature Algorithm (Gadsa)

In this paper GA based light weight faster version of Digital Signature Algorithm (GADSA) in wireless communication has been proposed. Various genetic operators like crossover and mutation are used to optimizing amount of modular multiplication. Roulette Wheel selection mechanism helps to select best chromosome which in turn helps in faster computation and minimizes the time requirements for DSA. Minimization of number of modular multiplication itself a NP-hard problem that means there is no polynomial time deterministic algorithm for this purpose. This paper deals with this problem using GA based optimization algorithm for minimization of the modular multiplication. Proposed GADSA initiates with an initial population comprises of set of valid and complete set of individuals. Some operators are used to generate feasible valid offspring from the existing one. Among several exponents the best solution reached by GADSA is compared with some of the existing techniques. Extensive simulations shows competitive results for the proposed GADSA.

A graph coloring constructive hyper-heuristic for examination timetabling problems

In this work we investigate a new graph coloring constructive hyper-heuristic for solving examination timetabling problems. We utilize the hierarchical hybridizations of four low level graph coloring heuristics, these being largest degree, saturation degree, largest colored degree and largest enrollment. These are hybridized to produce four ordered lists. For each list, the difficulty index of scheduling the first exam is calculated by considering its order in all lists to obtain a combined evaluation of its difficulty. The most difficult exam to be scheduled is scheduled first (i.e. the one with the minimum difficulty index). To improve the effectiveness of timeslot selection, a roulette wheel selection mechanism is included in the algorithm to probabilistically select an appropriate timeslot for the chosen exam. We test our proposed approach on the most widely used un-capacitated Carter benchmarks and also on the recently introduced examination timetable dataset from the 2007 International Timetabling Competition. Compared against other methodologies, our results demonstrate that the graph coloring constructive hyper-heuristic produces good results and outperforms other approaches on some of the benchmark instances.

Adaptive search in discrete limit analysis problems

The limit analysis of a segmental arch is considered. A collapse work rate minimisation problem is formulated, with constraints on the minimisation that incorporate a mesh description of geometric compatibility based on Kirchhoff’s network laws. A single quadratic objective function and a set of linear constraint equations are used to perform a limit analysis that considers both rotational and frictional failures in the arch. For comparison with a tested linear optimisation technique for arch analysis, the quadratic objective function is reduced to a linear function, and a Genetic Algorithm (GA) is adopted for the solution method. Ordinal ranking is based on the geometric suitability and energy requirements of a collapse mechanism. The solution set is then driven towards efficiency by the GA. The GA uses a roulette wheel selection mechanism based on the fitness of an individual to select parents for subsequent generations. Goals are dynamically redefined as the optimal solutions in a population improve. A conventionally challenging problem shows the solving efficiency of the GA formulation.