Memetic Evolutionary Algorithm Research Articles

Opportunistic UAV Deployment for Intelligent On-Demand IoV Service Management

Due to the current improvement in self-driving cars and the extensive focus and research on the topic of the Internet of Vehicles (IoV), the near future may behold a great revolution in the automotive industry as cars become fully autonomous. This change entails a considerable amount of data to be transferred from internet of things (IoT) devices, such as radars, sensors, and actuators. Consequently, overwhelming the existing infrastructure, namely cloud, and Road Side Units (RSU), reduces the quality of service (QoS) experienced by vehicular users. Accordingly, this paper contributes in proposing a new architecture for using Unmanned Ariel Vehicles (UAVs) and On-Boarding Units (OBUs) working in collaboration to achieve a significantly improved QoS. The proposed framework offers an end-to-end solution for master election, cluster management and recovery, vehicle selection, service placement, and accurate localization of vehicles. A QoS improvement is possible through an efficient cluster formation and placement solution that assigns lightweight services, as containers, to OBUs and UAVs while meeting various objectives. The efficiency of the proposed scheme originates from the use of the evolutionary Memetic Algorithm that 1) respects the mobility and energy constraints of UAVs and OBUs, 2) meets the user demands, and 3) uses machine learning for the accurate localization of vehicles. Our experiments using the Mininet-Wifi and SUMO simulators show at least 30% improvement in terms of QoS compared to a state-of-the-art solution.

A Hybrid Discrete Memetic Algorithm for Solving Flow-Shop Scheduling Problems

Flow-shop scheduling problems are classic examples of multi-resource and multi-operation scheduling problems where the objective is to minimize the makespan. Because of the high complexity and intractability of the problem, apart from some exceptional cases, there are no explicit algorithms for finding the optimal permutation in multi-machine environments. Therefore, different heuristic approaches, including evolutionary and memetic algorithms, are used to obtain the solution—or at least, a close enough approximation of the optimum. This paper proposes a novel approach: a novel combination of two rather efficient such heuristics, the discrete bacterial memetic evolutionary algorithm (DBMEA) proposed earlier by our group, and a conveniently modified heuristics, the Monte Carlo tree method. By their nested combination a new algorithm was obtained: the hybrid discrete bacterial memetic evolutionary algorithm (HDBMEA), which was extensively tested on the Taillard benchmark data set. Our results have been compared against all important other approaches published in the literature, and we found that this novel compound method produces good results overall and, in some cases, even better approximations of the optimum than any of the so far proposed solutions.

Memetic evolutionary algorithms to design optical networks with a local search that improves diversity

The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost.

Mutation testing in test suite generation using separate bacterial memetic evolutionary algorithm in IoT

The security of the Internet of Things (IoT) is becoming more important as it is used more frequently and widely in our daily lives. Better security analysis tools are necessary for IoT applications in order to assure information security. Therefore, a framework for evaluating taint-flow analysis tools in the context of IoT applications is presented. In various strategies, the mutation testing has been found in detecting the faults in the test case. Fault-based checking is considered as mutation testing because the flaws are identified actively in the code to ensure that the programmed functions correctly. If the flaws in the programmed are not identified, it indicates that program requires debugging for effective functioning. Testing has made a significant contribution to a set of approaches, tools, improvements, and perfect results; however, it is an expensive test leading to increased bug-finding practices. Automated tools and minimization methods were launched to decrease the expenses of mutation testing. The Separate Bacterial Memetic Evolutionary Algorithm (SBMEA) was examined in this study effort as a worldwide seeker. The suggested algorithm strengthens the reduction cost of mutation testing in all possible ways. It's able to achieve the minimum test case and mutants score for computational and cost, here the enhanced of Memetic algorithm were proposed. The present system improved the surrogate enhancement by lowering the expense and eliminating mutants in the snippets. The Separate Bacterial Memetic Evolutionary Algorithm (SBMEA) method of Memetic algorithm was performed the high mutation score in all testing of java snippets. Compared to proposed algorithm the execution time is saved in 6.95 s better than existing algorithm. It consists of evolution of strategy and program solver and increase the computational efficiency of the solution method. Here the Separate Bacterial Memetic Evolutionary Algorithm (SBMEA) method of Memetic algorithm is capable of lowering computational costs and eliminating all mutants, its best for reduction cost and for mutation testing.

Optimized one-shot neural architecture search for skin cancer classification

Skin cancer is the world’s fifth most prevalent cancer. Skin cancer is seen in one out of every three cancers. The quality of early and precise skin cancer detection is quite difficult. Some studies have been conducted on the computerized detection of malignancy in skin lesion images. The manual tuning of network architecture design is heavily reliant on domain expertise and is tough. In the present research, a one-shot neural architecture search technique is proposed that boost the search efficiency by modeling NAS as a one-shot training process of an over-parameterized supernet. As a result, various architectures can be derived from the supernet, and share the same weights. An evolutionary memetic algorithm employing local search was proposed to optimum the network architecture, which yields minimal computation time and accurate classification of benign and malignant skin lesions. Our search algorithm comprises all conceivable networks in each solution space. This approach prevents the neural architecture from being scratched. The proposed work proved efficient in ResNet search space with better outcomes than manually created networks on the ISBI dataset, which resulted in an accuracy of 93.26% and the computation cost is decreased. The search process also lasts an average of 18 h on a graphical processing unit.

Energy-Efficient Markov-Based Lifetime Enhancement Approach for Underwater Acoustic Sensor Network

The multihop underwater acoustic sensor network (M-UASN) collects oceanographic data at different depths. Due to the harsh underwater environment, the route is a major research problem. In this article, the routing path from source to sink is adapted by the vector-based forwarding (VBF) protocol. In VBF, based on the vector size, the packets are transmitted within the pipe from hop to hop. The limitation is that every node inside the pipe vector receives the same packets. That results in a waste of battery energy and, in turn, reduces the lifetime of the acoustic node. To enhance, in this article, it is divided into two parts. The first part is that the first hop nodes from the source are optimally divided into subsets such that all the second hop nodes will receive packets from each subset. This optimal route cover subset is identified with an evolutionary memetic algorithm. The election of subset is done through a voltage reference model, and the battery voltage is modeled mathematically and the role of the nodes is given based on the voltage profile and Markov probability approach. This method enhances the lifetime of the underwater acoustic network when compared with the VBF algorithm. The proposed model also provides improved throughput and equal load sharing. The results are compared with VBF, quality-of-service aware evolutionary routing protocol (QERP), and multiobjective optimized opportunistic routing (BMOOR).

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm.

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.

Effect of the initial population construction on the DBMEA algorithm searching for the optimal solution of the traveling salesman problem

There are many factors that affect the performance of the evolutionary and memetic algorithms. One of these factors is the proper selection of the initial population, as it represents a very important criterion contributing to the convergence speed. Selecting a conveniently preprocessed initial population definitely increases the convergence speed and thus accelerates the probability of steering the search towards better regions in the search space, hence, avoiding premature convergence towards a local optimum. In this paper, we propose a new method for generating the initial individual candidate solution called Circle Group Heuristic (CGH) for Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is built with aid of a simple Genetic Algorithm (GA). CGH has been tested for several benchmark reference data of the Travelling Salesman Problem (TSP). The practical results show that CGH gives better tours compared with other well-known heuristic tour construction methods.

Toward robust and energy-efficient clustering wireless sensor networks: A double-stage scale-free topology evolution model

Establishing a scale-free topology is widely regarded as one of the effective methods that can significantly improve the fault tolerance performance of wireless sensor networks (WSNs). However, the scale-free topology is very vulnerable to malicious attacks in which the nodes with high importance are attacked first, and its energy efficiency is also unsatisfactory. Therefore, how to build a comprehensively robust and energy-efficient WSN becomes a challenging issue. To address this issue, the paper proposes a Robust and Energy-efficient Scale-free double stage Topology evolution model (REST). In the first stage of REST, a scale-free backbone topology is built to achieve good fault tolerance, and in the second stage, the backbone topology is optimized using a memetic evolutionary algorithm to achieve good intrusion tolerance and energy efficiency. The topology constructed by REST is organized in a clustering manner, which is applicable to most task scenarios. Analytical and simulation results have demonstrated that REST can obtain a more satisfactory topological structure spending less time than existing algorithms.

An Automatic Credit Scoring Strategy (ACSS) using memetic evolutionary algorithm and neural architecture search

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments.

A Hybrid Discrete Bacterial Memetic Algorithm with Simulated Annealing for Optimization of the Flow Shop Scheduling Problem

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services

Observing the headway in vehicular industry, new applications are developed demanding more resources. For instance, real-time vehicular applications require fast processing of the vast amount of generated data by vehicles in order to maintain service availability and reachability while driving. Fog devices are capable of bringing cloud intelligence near the edge, making them a suitable candidate to process vehicular requests. However, their location, processing power, and technology used to host and update services affect their availability and performance while considering the mobility patterns of vehicles. In this paper, we overcome the aforementioned limitations by taking advantage of the evolvement of On-Board Units, Kubeadm Clustering, Docker Containerization, and micro-services technologies. In this context, we propose an efficient resource and context aware approach for deploying containerized micro-services on on-demand fogs called Vehicular-OBUs-As-On-Demand-Fogs. Our proposed scheme embeds (1) a Kubeadm based approach for clustering OBUs and enabling on-demand micro-services deployment with the least costs and time using Docker containerization technology, (2) a hybrid multi-layered networking architecture to maintain reachability between the requesting user and available vehicular fog cluster, and (3) a vehicular multi-objective container placement model for producing efficient vehicles selection and services distribution. An Evolutionary Memetic Algorithm is elaborated to solve our vehicular container placement problem. Experiments and simulations demonstrate the relevance and efficiency of our approach compared to other recent techniques in the literature.

Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment

With the increasing number of IoT devices, fog computing has emerged, providing processing resources at the edge for the tremendous amount of sensed data and IoT computation. The advantage of the fog gets eliminated if it is not present near IoT devices. Fogs nowadays are pre-configured in specific locations with pre-defined services, which limit their diverse availabilities and dynamic service update. In this paper, we address the aforementioned problem by benefiting from the containerization and micro-service technologies to build our on-demand fog framework with the help of the volunteering devices. Our approach overcomes the current limitations by providing available fog devices with the ability to have services deployed on the fly. Volunteering devices form a resource capacity for building the fog computing infrastructure. Moreover, our framework leverages intelligent container placement scheme that produces efficient volunteers’ selection and distribution of services. An Evolutionary Memetic Algorithm (MA) is elaborated to solve our multi-objective container placement optimization problem. Real life and simulated experiments demonstrate various improvements over existing approaches interpreted by the relevance and efficiency of (1) forming volunteering fog devices near users with maximum time availability and shortest distance, and (2) deploying services on the fly on selected fogs with improved QoS.

Memetic Evolution for Generic Full-Body Inverse Kinematics in Robotics and Animation

In this paper, a novel and fast memetic evolutionary algorithm is presented which can solve fully constrained generic inverse kinematics with multiple end effectors and goal objectives, leaving high flexibility for the design of custom cost functions. The algorithm utilizes a hybridization of evolutionary and swarm optimization, combined with the limited-memory-Broyden-Fletcher-Goldfarb-Shanno with bound constraints algorithm for gradient-based optimization. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided, scaling well even for greatly higher degree of freedom. The algorithm provides a general framework for bounded continuous optimization which only requires two parameters for the number of individuals and elites to be set, and supports adding additional goals and constraints for inverse kinematics, such as minimal displacement between solutions, collision avoidance, or functional joint relations. Experimental results on several industrial and anthropomorphic robots as well as on virtual characters demonstrate the algorithm to be applicable for solving complex kinematic postures for different challenging tasks in robotics, human-robot interaction and character animation, including dexterous object manipulation, collision-free full-body motion, as well as animation post-processing for video games and films. Implementations are made available for Unity3D and robot operating system.

Underwater Acoustic Sensor Node Scheduling using an Evolutionary Memetic Algorithm

Underwater Acoustic Sensor Networks (UWASNs) play an important role in monitoring the aqueous environment which has created a lot of interest for researchers and scientists. Utilization of underwater acoustic sensor node (UASN) scheduling for transmission remains, due to the limited acoustic bandwidth available, a challenge in such an environment. One of the methods to overcome this problem is to efficiently schedule UASN data using time division multiple access (TDMA) protocols the parallel transmissions, simultaneously avoiding interference. The paper shows how to optimize the utilization of acoustic sensor node bandwidth by maximizing the possible node transmissions in the TDMA frame and also by minimizing the node's turnaround wait time for its subsequent transmissions by using an evolutionary memetic algorithm (MA). The simulation of MA-TDMA proves that as the size of the network increases, every node in UWASN transmits with an average minimal turnaround transmission time. It also proves that as the TDMA cycle repeats, the overall network throughput gets maximized by increasing the possible node transmissions in the MA-TDMA frame.

Enhanced discrete bacterial memetic evolutionary algorithm - An efficacious metaheuristic for the traveling salesman optimization

In this paper we present a novel universal metaheuristic, Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is based on the combination of the Bacterial Evolutionary Algorithm and local search techniques, used for solving NP-hard optimization problems. The algorithm was tested on a series of symmetric Traveling Salesman Problems (TSP) and Traveling Salesman Problem with time windows (TSPTW) benchmarks.The size of the symmetric TSP benchmarks went up to 5 000 cities. In all cases the DBMEA algorithm produced optimal or near-optimal solutions and the difference from the known best values was within 0.16%. While for large size problems it was much faster than the Concorde solver, it was found to be slower compared to the Helsgaun-Lin-Kernighan heuristic, which is the most efficient TSP solver method.With some slight modifications the same algorithm was also tested on TSP with time windows (TSPTW) benchmark instances. In most cases the DBMEA procedure found the known best solutions, and it was again the second fastest method compared with the state-of-the-art techniques for the TSPTW.DBMEA is called efficacious because it is a universal method. It can be efficiently applied to various NP-hard optimization problems and, as in all cases, it results in the optimal or a very near-optimal solutions, while its runtime is very predictable in terms of the size of the problem, and the topology of the instance does not affect its runtime significantly. Even though heuristics developed for a particular type of problem might perform better for that restricted class, our novel method proposed here is universally applicable and may be deployed successfully for optimizing other discreet NP-hard graph search and optimization problems as well.

An effective Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem

An effective Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem

Evolutionary memetic algorithms supported by metaheuristic profiling effectively applied to the optimization of discrete routing problems

Optimizing the routing of resources to multiple remote sites is a complex issue confronting many sectors of the gas an oil industries with significant cost implications. When significant numbers of sites are involved the optimum solutions become difficult to find and require complex algorithms to do so. Memetic algorithms (MA), combining multiple metaheuristics and heuristics that can be easily activated or deactivated offer a potentially effective and flexible approach to complex routing optimization problems. By combining MAs with the recently proposed tool of metaheuristic profiling (MHP) it is possible to establish and monitor the contributions of the component metaheuristics and heuristics in finding the lowest-distance tour solutions for routing problems. MHP also facilitates the identification of synergies between specific metaheuristics, potential conflicts or duplication among others, and computational time consumption issues with certain combinations. Applying MHP as a monitoring tool during the development of MAs helps to develop balanced algorithms combining multiple metaheuristics focused on specific tasks, such as exploring the global solution space and/or exploiting the space locally around specific solutions. Memetic algorithms make it possible to consider the classic evolutionary algorithms and other well-known heuristics each as components in a “toolbox” of metaheuristics/heuristics available to be combined and configured to form flexible, fit-for-purpose optimization tools. A routing memetic algorithm is described in detail and tested using well-studied examples of the travelling salesman problem (TSP). MHP is applied, using the Excel-VBA platform, to reveal the relative contribution of the nine metaheuristics involved in the routing MA developed here, which incorporates some of the metaheuristics derived from bat-flight principles. The study identifies how these metaheuristics function together with integrational synergies. The MHP information is displayed in graphic and tabular form, alongside the optimum values obtained from multiple executions of the algorithm to illustrate the guidance and level of insight that can be provided by the MHP technique. Memetic algorithms typically involve multiple control variables that can be (and often need to be) tuned to improve their efficiency in finding the optima of specific problems. This makes them flexible, but potentially time consuming to setup and operate. The successful application of the MA to the TSP routing problem suggests scope for its development to address more complex routing problems.

Monkey King Evolution: A new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline consumption optimization (find the least gasoline consumption path) for vehicle navigation. Although there are many simple and applicable optimization algorithms, such as particle swarm optimization variants (including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully Informed Particle Swarm, Comprehensive Learning Particle Swarm Optimization, Dynamic Neighborhood Learning Particle Swarm). These algorithms are less powerful than the proposed algorithm in this paper. 28 benchmark functions from BBOB2009 and CEC2013 are used for the validation of robustness and accuracy. Comparison results show that our algorithm outperforms particle swarm optimizer variants not only on robustness and optimization accuracy, but also on convergence speed. Benchmark functions of CEC2008 for large scale optimization are also used to test the large scale optimization characteristic of the proposed algorithm, and it also outperforms others. Finally, we use this algorithm to find the least gasoline consumption path in vehicle navigation, and conducted experiments show that the proposed algorithm outperforms A* algorithm and Dijkstra algorithm as well.

NSGA-II Algorithm with a Local Search Strategy for Multiobjective Optimal Design of Dry-Type Air-Core Reactor

Dry-type air-core reactor is now widely applied in electrical power distribution systems, for which the optimization design is a crucial issue. In the optimization design problem of dry-type air-core reactor, the objectives of minimizing the production cost and minimizing the operation cost are both important. In this paper, a multiobjective optimal model is established considering simultaneously the two objectives of minimizing the production cost and minimizing the operation cost. To solve the multi-objective optimization problem, a memetic evolutionary algorithm is proposed, which combines elitist nondominated sorting genetic algorithm version II (NSGA-II) with a local search strategy based on the covariance matrix adaptation evolution strategy (CMA-ES). NSGA-II can provide decision maker with flexible choices among the different trade-off solutions, while the local-search strategy, which is applied to nondominated individuals randomly selected from the current population in a given generation and quantity, can accelerate the convergence speed. Furthermore, another modification is that an external archive is set in the proposed algorithm for increasing the evolutionary efficiency. The proposed algorithm is tested on a dry-type air-core reactor made of rectangular cross-section litz-wire. Simulation results show that the proposed algorithm has high efficiency and it converges to a better Pareto front.