Elitist Genetic Algorithm Research Articles

Dynamic Hierarchical Caching Resource Allocation for 5G-ICN Slice

Network slicing and Multiple-Access Edge Computing (MEC) are key technologies in fifth-generation (5G) networks. The flexible programmability of network slicing and the decentralization of MEC facilitate the deployment of Information-Centric Networking (ICN). The caching feature of ICN can provide users with low-latency data services. Although many existing works have addressed the cache deployment problem or the cache optimization problem, most of them do not consider the issue of caching resource allocation in the dynamic and hierarchical environment. Dynamic deployment of cache nodes can improve the operator’s revenue as much as possible while accurately allocating the caching resources can reduce the user-requested latency. Therefore, in this study, a problem of the operator’s expected revenue maximization is presented in an environment combining dynamic deployment of the MECs and the caching-enabled node ICN-Gateway (ICN-GW). To solve this problem, we propose an optimal stopping theory (OST)-based dynamic hierarchical caching resources allocation (ODH-CRA) algorithm. The algorithm consists of three parts. Firstly, an Integer Linear Programming (ILP) solution is proposed to determine the optimal deployment of the MECs. This method determines the optimal location and number of the MECs by considering deployment costs and service requirement costs synthetically. Secondly, a redeployment technique based on the OST is proposed to determine the best redeployment time of the MECs according to the values of latency violations and the service latency requirements. Finally, an improved elite genetic algorithm (IEGA) is proposed to find the optimal solution of the hierarchical caching resource allocation. This method searches the optimal scheme by maximizing the operator’s revenue joint caching costs and energy consumption. Ultimately, we perform a series of simulation experiments to compare the proposed method’s performance to dynamic and hierarchical methods. Our solution can effectively reduce the latency for users’ requesting, improve the revenue of ICN Communication Service Provider (ICSP), and provide an effective caching resource allocation scheme for the next generation of Internet of Things (IoT) networks.

Developing a futuristic multi-objective optimization of the fuel management problems for the nuclear research reactors

Abstract In this paper, at the same time, two separate objectives and two safety and operational constraints are chosen to optimize fuel reloading pattern of a Material Testing Reactor (MTR), independently and coherently. This is one of the most difficult type of engineering problems as a constrained, non-continuous, combinatorial, and fully multi-objective optimization problem. Decision space is a non-continuous multimodal space restricted by both of the combinatorial and safety constraints. A smart software application and a robust hybrid algorithm have been developed to get Pareto optimal set with respect to both of the economy of irradiating utilizations and nuclear safety based on the heuristic soft computing. The hybrid algorithm is composed of a fast and elitist Multi-Objective Genetic Algorithm (MOGA) and a fast fitness function evaluating system based on the semi-deep learning cascade feed forward Artificial Neural Networks (ANNs). The smart software is used to produce database automatically required for the ANN training and test data. It can be also used to revise data accurately, impose further irradiating benefits or Operating Limits and Conditions (OLCs), and to advise the reactor supervisor on the most desire pattern based on the smart searches and filtering. The results are highly promising. For more details, optimization results dominate conventional operating core parameters, significantly. Also chosen OLCs are protected. Furthermore, this is very good practice to reach a fully developed practical application of the complex soft computing for the nuclear fuel management problems.

Stochastic Multi-objective Optimisation of Exoskeleton Structures

In this study, a structural optimisation problem, addressed through a stochastic multi-objective approach, is formulated and solved. The problem deals with the optimal design of exoskeleton structures, conceived as vibration control systems under seismic loading. The exoskeleton structure is assumed to be coupled to an existing primary inner structure for seismic retrofit: the aim is to limit the dynamic response of the primary structure to prevent structural damage. A non-stationary filtered Gaussian white noise stochastic process is taken as the seismic input. Design variables pertain to the mechanical properties (stiffness, damping) of the exoskeleton structure. Two concurrent and competing objective functions are introduced, in order to take into account not only safety performance but also economic cost considerations. The resulting trade-off is solved searching the Pareto front by way of a controlled elitist genetic algorithm, derived from the Non-dominated Sorting Genetic Algorithm-II. Sensitivities of Pareto fronts and Pareto optimal sets to different system parameters are finally investigated by way of a numerical application.

Charge Plan Model for Steelmaking-Continuous Casting Section

Pursuing the intellectualization of a steelmaking plant and developing a charge plan of the steelmaking-continuous casting section are critical in metallurgy engineering. Herein, we aim to develop a charge plan model based on the operation of the steelmaking-continuous casting section to minimize the penalty values of residual materials; of a contract not selected and the penalty values that is caused by the difference in steel grades, the width and the delivery time between slabs in the same charge. We introduce an improved elitist genetic algorithm (IEGA), define the matching chromosome coding and decoding strategies, and suggest improving the selection, crossover, and mutation operators. Finally, we verify the proposed model and algorithm on the production data of a real enterprise. We clarify the applicability of developing a charge plan based on model analysis and demonstrate the effectiveness of the IEGA through algorithm analysis.

A hybrid method integrating an elite genetic algorithm with tabu search for the quadratic assignment problem

The Quadratic Assignment Problem (QAP) is one of the most studied classical combinatorial optimization problems. QAP has many practical applications. Designing enhanced meta-heuristic approaches for the QAP is an active research area. In this work, we propose a hybrid algorithm (EGATS) that combines an elite genetic algorithm and tabu search to solve the QAP. In the optimization process, EGATS employs two kinds of elite crossovers, repeated 2-exchange mutation, and tabu search to strike a balance between exploitation and exploration. We evaluated the performance of EGATS through computational experiments on 135 well-known benchmark instances from the quadratic assignment problem library, QAPLIB. EGATS obtained the best-known solution for 131 instances. Compared to other popular meta-heuristic algorithms in the literature, EGATS is a competitive method for the QAP.

A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course

Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students’ interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin’s theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques.

An Evolutionary Multiagent Framework for Multiobjective Optimization

This article proposes an evolutionary multiagent framework of the co-operative co-evolutionary multiobjective model (CCMO-EMAS), specifically for equipment layout optimization in engineering. In this framework, each agent is set in a multiobjective cooperative co-evolutionary mode along with the algorithms and corresponding settings. In each iteration, agents are executed in turn, and each agent optimizes a subpopulation from system decomposition. Additionally, the collaboration mechanism is addressed to build complete solutions and evaluate individuals in the co-operative co-evolutionary algorithm. Each subpopulation is optimized once, and the corresponding agent is evaluated based on the improvement of the system memory. Moreover, the agent team is also evolved through an elite genetic algorithm. Finally, the proposed CCMO-EMAS framework is verified in a multimodule satellite equipment layout problem.

Dimensional and Layout Optimization Design of Multistage Gear Drives Using Genetic Algorithms

In the minimal weight/volume design of multistage gear drives, both the dimensional and layout parameters of gear pairs have a direct effect on the design result. A new optimization model that can carry out both dimensional- and layout-constrained optimization design for any number of stages of cylindrical gear drives simultaneously is proposed. The optimization design of a three-stage cylindrical gear drive is conducted as a design example to test the application of this model. In the attempt to solve this constrained optimization problem using an elitist genetic algorithm (GA), different constraint handling methods have a crucial effect on the optimal results. Thus, the results obtained by applying three typical constraint handling methods in GA one by one are analyzed and compared to figure out which one performs the best and find the optimal solution. Moreover, a more precise projection center distance (PCD) method to calculate the degree of interference constraint violation is proposed and compared with the usually used (0, 1) method. The results show that the proposed PCD method is a better one.

Fuzzy reliability redundancy optimisation with signed distance method for defuzzification using genetic algorithm

Consideration of impreciseness is more realistic for modelling of physical phenomena. This impreciseness can be considered in several ways like, interval/stochastic/fuzzy or mixture of these. In this work, we have taken for optimising of the system reliability of a redundancy allocation problem formulated from a complex network system with imprecise parameters in the form of trapezoidal fuzzy numbers (TrFN). The signed distance method has been used to defuzzify the fuzzy values. Then big-M penalty technique is used to transform the problem to unconstrained optimisation problem. To solve these problems, we have implemented the real coded elitist genetic algorithm (RCEGA) for integer variables with tournament selection, intermediate crossover and one neighbourhood mutation. For illustration, the five link bridge network system has been solved and the results have been presented.

On the convergence rate of the elitist genetic algorithm based on mutation probability

The Markov chain approach to the elitist genetic algorithm enables not only to prove its convergence to an equilibrium distribution, but also to establish its convergence rate. These convergence rates are based on the transition matrix of the Markov chain which models the algorithm. This paper improves existing estimates of the convergence rates of the elitist genetic algorithm and presents new ones based only on the mutation probability. Experimental results illustrate that, for a fixed mutation probability, the algorithm’s mean convergence time tends to remain unchanged as the crossover probability varies. On the other hand, the reciprocal is not observed.

Sequential optimisation of reconfigurable machine cell feeders and production sequence during lean assembly

ABSTRACTThis paper presents a manufacturing model for integrating reconfigurable machine cells as feeders of components to a multi-product assembly line exhibiting a prominent lean characteristic; synchronisation with Takt time. Two mathematical formulations in the form of combinatorial optimisation problems are developed to identify the best reconfiguration for the machines by optimum selection of the modules in the cells to minimise the total error with Takt time and then the optimum sequence of assembling the products to minimise the total reconfiguration time and effort. An elitist genetic algorithm (GA) meta-heuristic is employed twice in succession to search the optimal solutions for both the minimisation problems. The best machine configuration found from the first search is utilised further to identify the optimal assembly sequence. A hypothetical data set representing the features and parameters of the model in conformance with any typical mixed-model assembly is presented for illustration of computational procedure. Synchronisation among machine cells and tardiness are considered as performance evaluation measures to validate the effectiveness of the proposed model.

SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data

In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively.

Multi-objective optimum design of an aero engine rotor system using hybrid genetic algorithm

A working aero engine rotor system is subjected to multi-objective optimisation using Genetic Algorithm based optimisation. A Hybrid Genetic Algorithm (HGA) is introduced to reduce the weight and unbalance response of the rotor system with constrain on critical speed. The existing aero engine gear box casing vibration is found to be within the critical speed constraint and additional constraints are imposed to move the critical away from this zone. Bearing–pedestal model and Rayleigh damping model are used for accurate results. The optimisation resulted in Pareto optimal solutions and best solution selected using utopia point concept. The outcome of the paper is a comparative study which highlights the advantages of HGA over Controlled Elitist Genetic Algorithm (CEGA) and Goal Programming (GP).

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

Cost-effective resource segmentation in hierarchical mobile edge clouds

The fifth-generation (5G) network cloudification enables third parties to deploy their applications (e.g., edge caching and edge computing) at the network edge. Many previous works have focused on specific service strategies (e.g., cache placement strategy and vCPU provision strategy) for edge applications from the perspective of a certain third party by maximizing its benefit. However, there is no literature that focuses on how to efficiently allocate resources from the perspective of a mobile network operator, taking the different deployment requirements of all third parties into consideration. In this paper, we address the problem by formulating an optimization problem, which minimizes the total deployment cost of all third parties. To capture the deployment requirements of the third parties, the applications that they want to deploy are classified into two types, namely, computation-intensive ones and storage-intensive ones, whose requirements are considered as input parameters or constraints in the optimization. Due to the NP-hardness and non-convexity of the formulated problem, we have designed an elitist genetic algorithm that converges to the global optimum to solve it. Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.

Fast converging elitist genetic algorithm for knot adjustment in B-spline curve approximation

Abstract B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.

Multi-Objective Optimization Assisted Network Condition Aware QOS-Routing Protocol for MANETS: MNCQM

The exponential rise in wireless communication systems and allied applications has revitalized academiaindustries to achieve more efficient data transmission system to meet Quality-of-Service (QoS) demands. Amongst major wireless communication techniques, Mobile Ad-hoc Network (MANET) is found potential to provide decentralized and infrastructure less communication among multiple distributed nodes across network region. However, dynamic network conditions such as changing topology, congestion, packet drop, intrusion possibilities etc often make MANET’s routing a tedious task. On the other hand, mobile network feature broadens the horizon for intruders to penetrate the network and causes performance degradation. Unlike classical MANET protocols where major efforts have been made on single network parameter based routing decision, this research paper proposes a novel Elitist Genetic Algorithm (EGA) Multi-Objective Optimization assisted Network Condition Aware QoS-Routing Protocol for Mobile Ad-hoc Networks (MNCQM). Our proposed MNCQM protocol exhibits two phase implementation where at first it performs node-profiling under dynamic network topology for which three factors; irregular MAC information exchange, queuing overflow and topological variations have been considered. Towards this objective node features like Packet Forwarding Probability (PFP) at the MAC layer, Success Probability of Data Transmission (SPDT) of a neighboring node, and Probability of Successful Data Delivery (PSDD) have been obtained to estimate Node-Trustworthiness Index (NTI), which is further used to eliminate untrustworthy nodes. In the second phase of implementation, a novel Evolutionary Computing assisted nondisjoint best forwarding path selection model is developed that exploits node’s and allied link’s connectivity and availability features to identify the quasi-sub-optimal forwarding paths. EGA algorithm intends to reduce hop-counts, connectivity-loss and node or link unavailability to estimate best forwarding node. One key feature of the proposed model is dual-supplementary forwarding path selection that enables alternate path formation in case of link outage and thus avoids any iterative network discovery phase.

Multi-objective optimisation of a small aircraft turbine engine rotor system with self-updating Rayleigh damping model and frequency-dependent bearing-pedestal model

A self-updating Rayleigh damping model and frequency-dependent bearing-pedestal model for multi-objective optimisation is presented through this paper and is applied for a small turbine engine rotor system for aircraft application. This engine is used as an auxiliary power unit on aircraft. The Rayleigh damping model and frequency-dependent bearing pedestal model are verified by carrying out experiments on this auxiliary power unit rotor system. The novel self-updating feature calculates the Rayleigh damping coefficients and frequency-dependent bearing-pedestal stiffness for each chromosome and modifies rotor system equation of motion for computing the objectives during multi-objective optimisation for each chromosome. This novel model is used for multi-objective optimisation of auxiliary power unit rotor system. The unbalance response and weight are minimised subjected to critical speed constraint. Controlled elitist genetic algorithm is used for the optimisation resulting in Pareto optimal solutions and the acceptable solution is identified as the solution close to Utopia point. The results are compared with the constant Rayleigh damping model. The new model has produced an accurate optimum solution superior to constant Rayleigh damping model.

Multi-UAV Mission Allocation under Constraint

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.

Stability Constrained Economic Operation of Islanded Droop-Controlled DC Microgrids

This paper proposes a method to determine optimal droop values of dispatchable distributed generation (DG) units in an islanded droop-controlled dc microgrid. Objectives are to minimize fuel cost and maximize small signal stability of the system. In this paper, a detailed state-space model of an islanded droop-controlled dc microgrid including the converter, network parameters, and network interconnection is developed. The developed model is then used to obtain the dominant eigen value of the system. Particle swarm optimization is used to determine the optimal droop values of the dispatchable DG units in order to simultaneously reduce the fuel cost, increase system damping, and push the most dominant eigen value away from the imaginary axis to the left of the s -plane. The bi-objective optimization is solved using a fuzzy max–min based approach. The proposed method is validated on a sample three-bus droop-controlled DCMG test system. Comparison of the result obtained by the fuzzy max–min approach with that of controlled elitist genetic algorithm is also presented in this paper.