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Abstract Nodes in performance heterogeneous wireless sensor networks (HWSNs) often have varying levels of available energy, storage space, and processing power due to the network’s limited resources. Additionally, coverage redundancy and channel conflicts may adversely influence the quality of service in a network when many nodes have been deployed at once. Energy as a major constrained resource requires an effective energy-efficient scheduling mechanism to balance node energy consumption to extend the network lifespan. Therefore, this research proposes an energy-efficient scheduling technique, IMA–NCS-3D for three-dimensional HWSNs on the basis of an improved memetic algorithm and node cooperation strategy. A multi-objective fitness function is created to encode the active and inactive states of nodes as genes, and the optimal scheduling set of the network is built via selection, crossover, variation, and local search. This phase of the process is known as node scheduling. Node-to-node cooperation solutions are offered during data transmission to deal with unforeseen traffic abnormalities and reduce congestion and channel conflicts when traffic volumes are high. Simulation results show that IMA–NCS-3D has superior scheduling capability, cross-network load balancing capability, and a longer network lifespan than other current coverage optimization approaches.

Floorplanning is a crucial part of very large-scale integration (VLSI) physical design flow. It primarily determines the position of the blocks on a chip by considering the area, the total wirelength, etc., in light of several real-world limitations such as delay, price, and chip performance. Adopting B*-tree representation, this paper proposes a novel dual-population adaptive hybrid memetic algorithm called DPAHMA to handle the VLSI floorplanning problem effectively by optimizing the chip area and the total wirelength. Three main ideas are presented in this paper, including new definitions of crossover and mutation operators based on B*-tree encoding that overcome the shortcomings of the existing method, such as overly complicated operations on binary trees and a lack of diversity; a dynamic self-adjusting objective function, namely WeightDS, which is able to find solutions more suitable for the user-specified weight; and a main-auxiliary population mechanism by which a candidate population is introduced to assist the normal population in the global search phase. To make full use of the information obtained by the local search method, the candidate population keeps its high-quality solutions. The individuals from the candidate population crossover with the individuals from the normal population before the end of each iteration to obtain higher-quality solutions as much as possible. Experimental results for MCNC and GSRC benchmarks show that DPAHMA obtains floorplans effectively with better area and total wirelength than those of the state-of-the-art floorplanners. [ABSTRACT FROM AUTHOR]

In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate system, alongside the creation of a three-phase orbital manoeuvre model. A Non-dominant Sorting Adaptive Memetic (NSAM) algorithm is proposed in this paper, which is a two-layer multi-objective optimization algorithm that retains the advantages of evolutionary algorithms based on the population's evolution and has an excellent local optimization ability of local search algorithms. The proposed algorithm can be used to solve multi-objective optimization problems. By comparing the target observation results before and after the satellite reconstruction simulation, it can be concluded that the orbital manoeuvring can effectively improve the observation probability and observation duration of the target at a certain speed. The orbital manoeuvre model created in this paper provides a certain methodical support for the tracking problem of moving targets at sea. [ABSTRACT FROM AUTHOR]

The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. [ABSTRACT FROM AUTHOR]

Today's popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adultthemed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. [ABSTRACT FROM AUTHOR]

In this research, a multi-objective model is presented for the optimization of financial flows in a sustainable supply chain closedloop. The objectives of this model include maximizing profits, minimizing environmental and social impacts, and maximizing financial ratios. The supply chain used in this research is the supply chain of production and recycling of polymer parts. As for modeling and optimization, the memetic hybrid meta-heuristic algorithm has been used. The memetic algorithm is an algorithm that is obtained by combining a genetic algorithm and a refrigeration simulation algorithm. The proposed model includes production planning, distribution, and waste collection, and is designed for multi-period and multi-product. The financial analysis parameters of the model include the current ratio, the debt-to-equity ratio, the net profit margin, the cash ratio, and the rate of return on investment. The analysis of the results shows that consideration of financial goals and indicators will lead to improved profitability. The findings also indicate that the firms can enhance their economic value through better legitimacy stemming from adopting social responsibilities and environmental concerns.

Among the growth and development of businesses, managing financial flows and increasing its effectiveness is an important achievement that causes value stability, efficiency of systems and procedures throughout the chains of Business. Therefore, paying attention to uncontrollable and controllable variables that lead to value creation is inevitable. The purpose of this study is to study the impact of social and biological factors (production and recycling of polymer parts) of environment on the value creation of the company along the supply chain. Also the key role of financial ratios has also been considered in this regard.The present research is of quantitative and applied types and is based on mathematical modeling. It is the result of a combination of genetic algorithms and Simulated Annealing. The financial analysis parameters of the model include current ratios, debt to equity, Instantaneous ratio, net profit margin, cash ratio and rate of return.The analysis of the results shows that considering financial goals and indicators leads to improved profitability and by removing financial indicators from the model, profitability is reduced. Therefore,this is means that the environmental and social performance of the supply chain is improved.Companies can pay special attention to social issues and environmental factors along with their profitability Increase their economic value. Profitability can also be improved by exposing social responsibilities and the mission of environmental protection.

This work aims to deal with the distributed heterogeneous unrelated parallel machine scheduling problem (DHUPMSP) with minimizing total tardiness (TDD) and makespan. To solve this complex combinatorial optimization problem, this work proposed a knowledge and Pareto-based memetic algorithm (KPMA) which contains the following features: 1) four heuristic rules are designed including the shortest processing time rule, the minimum factory workload rule, the minimum machine finish time rule, and the earliest due date rule. Meanwhile, a hybrid heuristic initialization is developed to construct a population with great convergence and diversity; 2) four problem feature-based heuristic neighborhood structures are designed to increase the success rate of local search; and 3) a simple elite strategy is developed to enhance the usage of historical elite solutions. Finally, to evaluate the performance of KMPA, it is compared to five state-of-art and run on 20 instances with different scales. The results of numerical experiments show that the proposed hybrid heuristic initialization can efficiently save computation resources to improve the initialized convergence. In addition, the knowledge-based neighborhood structures can vastly accelerate exploration. Moreover, the elite strategy can efficiently improve the diversity of the final non-dominated solutions set. The proposed KPMA has better performance than the state-of-art and has a strong ability to solve DHUPMSP.

Accurate localization is a critical task in underwater navigation. Typical localization methods use a set of acoustic sensors and beacons to estimate relative position, whose geometric configuration has a significant impact on the localization accuracy. Although there is much effort in the literature to define optimal 2D or 3D sensor placement, the optimal sensor placement in irregular and constrained 3D surfaces, such as autonomous underwater vehicles (AUVs) or other structures, is not exploited for improving localization. Additionally, most applications using AUVs employ commercial acoustic modems or compact arrays, therefore the optimization of the placement of spatially independent sensors is not a considered issue. This article tackles acoustic sensor placement optimization in irregular and constrained 3D surfaces, for inverted ultra-short baseline (USBL) approaches, to improve localization accuracy. The implemented multi-objective memetic algorithm combines an evaluation of the geometric sensor's configuration, using the Cramer-Rao Lower Bound (CRLB), with the incidence angle of the received signal. A case study is presented over a simulated homing and docking scenario to demonstrate the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

Nodes in performance heterogeneous wireless sensor networks (HWSNs) often have varying levels of available energy, storage space, and processing power due to the network's limited resources. Additionally, coverage redundancy and channel conflicts may adversely influence the quality of service in a network when many nodes have been deployed at once. Energy as a major constrained resource requires an effective energy-efficient scheduling mechanism to balance node energy consumption to extend the network lifespan. Therefore, this research proposes an energy-efficient scheduling technique, IMA–NCS-3D for three-dimensional HWSNs on the basis of an improved memetic algorithm and node cooperation strategy. A multi-objective fitness function is created to encode the active and inactive states of nodes as genes, and the optimal scheduling set of the network is built via selection, crossover, variation, and local search. This phase of the process is known as node scheduling. Node-to-node cooperation solutions are offered during data transmission to deal with unforeseen traffic abnormalities and reduce congestion and channel conflicts when traffic volumes are high. Simulation results show that IMA–NCS-3D has superior scheduling capability, cross-network load balancing capability, and a longer network lifespan than other current coverage optimization approaches. [ABSTRACT FROM AUTHOR]

In a bike-sharing system, the user route planning problem (URPP) is significant because it helps users to plan the route and to choose appropriate intermediate stations to transfer bikes, so as to reduce riding costs. To efficiently solve the URPP, this paper models it as a discrete optimization problem with constraints and proposes a knowledge learning and random pruning-based memetic algorithm (KLRP-MA), with three main contributions. First, the proposed URPP model is from the perspective of users and is suitable for not only static situations but also dynamic situations, which is novel from existing work that mainly focuses on the perspective of bike companies in a static situation. Second, three novel evolutionary operators named knowledge learning-based crossover, random pruning-based mutation, and two-phase local search are proposed and integrated into the KLRP-MA for dealing with the challenges of the URPP. Third, based on the dynamic URPP model, the KLRP-MA is further extended to a dynamic KLRP-MA (DyKLRP-MA) to quickly search for a reasonable route for users when the availability status of the bike stations changes over time, so as to deal with URPP in dynamic situations. Experimental results on 180 generated instances and 10 real-world instances show that when compared with the traditional and state-of-the-art methods, the KLRP-MA and the DyKLRP-MA can find the best solution in a short time. Moreover, in a dynamic situation, the DyKLRP-MA can quickly respond to changes and re-optimize the current planning route. [ABSTRACT FROM AUTHOR]

In this paper we consider multi-objective optimization problems over a box. Several computational approaches to solve these problems have been proposed in the literature, that broadly fall into two main classes: evolutionary methods, which are usually very good at exploring the feasible region and retrieving good solutions even in the nonconvex case, and descent methods, which excel in efficiently approximating good quality solutions. In this paper, first we confirm, through numerical experiments, the advantages and disadvantages of these approaches. Then we propose a new method which combines the good features of both. The resulting algorithm, which we call Non-dominated Sorting Memetic Algorithm, besides enjoying interesting theoretical properties, excels in all of the numerical tests we performed on several, widely employed, test functions. [ABSTRACT FROM AUTHOR]

As with the continuous improvement of the workshop automation rate and the importance in energy consumption, more and more enterprises not only need to make scheduling decision on production equipment, but also need to consider whether the scheduling of transportation equipment supports scheduling decisions on workshop production. At the same time, because both workshop production scheduling decision and transportation scheduling decision are NP-hard problems, it is necessary to design an efficient algorithm to improve productivity of the workshop. In order to solve this problem, firstly, based on the analysis of the problem structure, production environment and optimization objectives, a “manufacturing-transportation” multi-objective joint scheduling optimization mathematical model is established. By converting the energy consumption into the total transportation time objective of the transportation equipment, both total transportation time and makespan are taken as the optimization objectives. Secondly, based on the design idea of memetic algorithm (MA), non-dominated sorting genetic algorithm-II(NSGA-II) is employed as the basis framework of our new developed algorithm. An effective discrete encoding scheme of MO-MA, a new initialization method for initial population and a neighborhood search mechanism based on critical path are incorporated into our new proposed algorithm. Then the parameter design of the algorithm is completed through variance analysis. Finally, the proposed algorithm is compared and analyzed with other algorithms in the dimension of hypervolume and Set Coverage (SC), and advantages of the algorithm in solving this problem are verified.

For the green hybrid flow shop scheduling problem considering sequence dependent setup times (SDST) and transportation times, a mixed integer programming model is established with the objectives of minimizing the maximum completion time (makespan) and total energy consumption simultaneously, and an improved memetic algorithm is proposed with the problem characteristics. First of all, an encoding method combining the jobs sequence code at the first stage with the machine allocation code is designed to ensure that the algorithm can search the entire solution space to the greatest extent; then, a mixed population initialization method is designed to improve the quality of the initial population solution; thirdly, the crossover and mutation operators, and four neighborhood search strategies are designed to balance the global search and local search capabilities of the algorithm; finally, the effectiveness of the algorithm is verified by numerical experiments.

This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component.

Service composition is the process of combining a set of elementary or atomic services. The aim is to produce a new composite service to satisfy the user's request that cannot be satisfied by the atomic services. Combining multiple services is a complex problem that has been the subject of several research studies. The meta‐heuristic approaches are good techniques that have been used to solve several complex problems in various domains. These techniques are able to discover promising search regions and locate good quality solutions in reasonable time without exploring the whole solution space. In this paper, we deal with the problem of optimal web service composition by using meta‐heuristic approaches. Given a set of services and a set of tasks to be completed, the problem is to find the best set of services composition to complete all tasks where each service must be assigned to a given task. This problem can be modelled as a combinatorial optimization problem with a set of objective functions that need to be optimized. We search for a composite service that allows us to execute the considered tasks and offers the best quality of services (QoS). More precisely, we search for an execution plan that indicates for each task the assigned service. First, we propose a multi‐objective local search based meta‐heuristic (MO‐LS) and a multi‐objective genetic algorithm (MO‐GA) to handle our problem. Then we propose a multi‐objective memetic algorithm (MO‐MA) that combines the two methods LS and GA. The role of GA is to detect promising regions to be explored. The role of LS is to exploit efficiently the potential regions created by GA. Four objective functions are used to compute the Pareto optimal set of solutions. The main objective is to minimize cost and time and to maximize availability and reputation and produce a good composite service. The three proposed approaches namely MO‐LS, MO‐GA, and MO‐MA are evaluated on some datasets generated randomly and on the well‐known QWS dataset to select the best fit services in terms of maximum or minimum aggregated end‐to‐end QoS parameters. The numerical results are encouraging and demonstrate the effectiveness of the proposed MO‐MA for the web service composition. [ABSTRACT FROM AUTHOR]

Neural Architecture Search (NAS), that automates the design process of high-performing neural network architectures, is a multi-objective optimization problem. A single ideal architecture, that optimizes both predictive performance (e.g., the network accuracy) and computational costs (e.g., the model size, the number of parameters, the number of floating-point operations), does not exist. Instead, there is a Pareto front of multiple candidate architectures where each one represents an optimal trade-off between the competing objectives. Multi-Objective Evolutionary Algorithms (MOEAs) are often employed to approximate such Pareto-optimal fronts for NAS problems. In this article, we introduce a local search method, namely Potential Solution Improving (PSI), that aims to improve certain potential solutions on approximation fronts to enhance the performance of MOEAs. The main bottleneck in NAS is the considerable computation cost that incurs from having to train a large number of candidate architectures to evaluate their accuracy. Recently, the Synaptic Flow has been proposed as a metric that relatively characterizes the performance of deep neural networks without running any training epoch. We thus propose that our PSI method can make use of this training-free metric as a proxy for network accuracy during local search steps. We conduct experiments with the well-known MOEA Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the training-free PSI local search in solving NAS problems created from the standard benchmarks NAS-Bench-101 and NAS-Bench-201. Experimental results confirm the efficiency enhancements brought about by our proposed method, which reduces the computational cost by four times compared to the baseline approach. The source code for the experiments in the article can be found at: https://github.com/ELO-Lab/MOENAS-TF-PSI. [ABSTRACT FROM AUTHOR]

The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and its importance in manufacturing and engineering processes. Here we consider a variant of the problem where uncertainty in operation processing times is modeled using triangular fuzzy numbers. Our objective is to minimize the total energy consumption, which combines the energy required by resources when they are actively processing an operation and the energy consumed by these resources simply for being switched on. To solve this NP-Hard problem, we propose a memetic algorithm, a hybrid metaheuristic method that combines global search with local search. Our focus has been on obtaining an efficient method, capable of obtaining similar solutions quality-wise to the state of the art using a reduced amount of time. To assess the performance of our algorithm, we present an extensive experimental analysis that compares it with previous proposals and evaluates the effect on the search of its different components. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability of services. The topology of the wireless sensor networks acts driven factor for network efficiency which can be effectively maintained by performing the clustering process effectively. More solutions and clustering algorithms have been offered by various researchers, but the concern of reduced efficiency in the routing process and network management still exists. This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks. The memetic algorithm employs a local searching methodology to mitigate the premature convergence, while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain (i.e.,) best cluster head in the wireless sensor networks. The proposed hybrid algorithm is compared with the state of art clustering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time, energy consumption, throughput, etc. [ABSTRACT FROM AUTHOR]

In this paper, a novel mathematical model is developed for the distributed multi-agent network scheduling problem in a dynamic job shop environment with availability constraints and new job arrivals. In a dynamic collaborative-competitive environment, a number of factories with independent ownership are merged to form a multi-agent production network in which each production agent, despite participation, seeks to optimize its own objective function as a primary priority. To minimize the makespan and the total energy consumption, the ε-constraint approach is used. Since the problem is NP-hard, a memetic algorithm based on a non-dominated sorting genetic algorithm-II and local search are proposed. Finally, the proposed algorithm is compared with a hybrid Pareto-based tabu search algorithm (HPTSA). The obtained results show that in large-size instances, our proposed algorithm outperforms the HPTSA. [ABSTRACT FROM AUTHOR]

Maximum-entropy probability models (MEPMs) have been widely used to reveal the structure of genetic regulatory networks (GRNs). However, owing to the inherent network sparsity and small sample size, most of the existing MEPMs use convex optimization to approximate the inference of GRNs which tend to be trapped in less accurate local optimal solutions. Evolutionary algorithms (EAs) can help address this issue thanks to their superior global search capability, yet the conventional EA-based methods cannot handle the sparsity of GRNs efficiently. To overcome this problem, we propose a multi-objective memetic algorithm in this study to infer the sparse GRNs with MEPMs. Particularly, the target inferring problem is formulated as a multi-objective optimization problem where the maximum entropy and the constraints of the MEPM are formulated as two objectives. We employ Graphical LASSO (Glasso) to generate prior knowledge for population initialization. The genetic operators are adopted to ensure the diversity and sparsity of the inferred GRNs. Local search based on the spatial relations among solutions and different Glasso results in the decision space is incorporated into the algorithm to improve the search efficiency. Experimental results on both simulated and real-world data sets suggest that the proposed method outperforms other state-of-the-art GRN inferring methods in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Community structures are sets of nodes that are densely linked with each other, reflecting the functional modules of real-world systems. Most classical works for community detection (CD) are based on the optimization of an objective function, namely modularity. However, it has been recently demonstrated that there exists a resolution limit in the modularity optimization based CD methods, i.e., the communities cannot be detected if their scales are smaller than a certain threshold. To overcome this resolution limit, in this paper, we propose a decomposition based multiobjective memetic algorithm (called MDMCD) for multiresolution CD (MCD) in complex networks, aiming to detect communities at multiple resolution levels. MDMCD first models the MCD problem as a multiobjective optimization problem (MOP) with two contradictory objectives, namely the intra-link ratio and inter-link ratio. Then, it devises a multiobjective memetic optimization framework that combines a decomposition based multiobjective evolutionary algorithm with a two-level local search to solve the modeled MOP. In this framework, the modeled MOP is first decomposed into a set of single-objective optimization subproblems, each of which corresponds to a CD problem in a certain resolution level. Subsequently, these subproblems are simultaneously optimized by the evolutionary operators and the local search, taking the network-specific knowledge into consideration. Finally, MDMCD returns a population of solutions in a single simulation run, reflecting the community divisions at multiple resolution levels. Experiments on both the simulated and real-world networks show the effectiveness of MDMCD in detecting multiresolution community structures. [ABSTRACT FROM AUTHOR]

In real-world scenes involving multimodal multiobjective optimization, there may exist different Pareto optimal sets (PSs) for the same Pareto front (PF), and some PFs even need to reserve all PSs, including local and global PSs. Most existing multimodal multiobjective optimization algorithms often distinguish solutions according to their diversity and convergence performances in the objective space. However, they pay little attention to the potential of solutions in the decision space. To solve this issue, a novel multimodal multiobjective memetic algorithm based on a local detection mechanism and a clustering-based selection strategy is proposed in this paper. To reserve more global and local PSs in the decision space, a density-based clustering method is adopted in the local detection mechanism, assisting in collecting solutions in the local clusters. Furthermore, in the clustering-based selection strategy, two different clustering methods are applied to different situations according to the ratio of the local optimal solutions. Extensive experimental results and performance comparisons with state-of-the-art algorithms show the superiority of our proposed algorithm. [ABSTRACT FROM AUTHOR]

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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.

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm.

The article aims to address a research gap concerning the adequate configuration of a Memetic Algorithm adapted to solve the Job-Shop Scheduling Problem. The goal was accomplished by means of conducting a comparative study of 16 variants of a Memetic Algorithm, characterised with different places of hybridisation and local search methods applied. The study involved the solution of eleven instances of JSP and the comparison of the results with the results achieved with an Evolutionary Algorithm lacking the mechanism of hybridisation and selected local search methods. The utilitarian significance of the problem also involved the use of assessment measures intended for both practical applications and research purposes. [ABSTRACT FROM AUTHOR]

A complex system is a system that has many components that are interdependent and appear as a whole and exhibit organized behavior. Community structure detection is an optimization problem in complex networks that involves searching for communities belonging to a network that shares nodes of a similar community with standard features. In this paper, we use a multi-agent memetic algorithm to detect the structure of the community in complex networks by optimizing the amount of modularity and calling it MAMA-Net. In the multi-agent memetic algorithm, agents are placed in a network-like environment to detect the community. Local search is used to find solution space. Having a local search in the memetic algorithm allows each member of the population to increase its evaluation function based on the suitability of its neighbors and achieve the desired result in minimum time. We compare the performance of MAMA-Net in detecting community structure with some standard algorithms. Both real-world and synthetic benchmark networks are used to evaluate the performance of the proposed method. The results show that MAMA-Net could detect communities more accurately than other comparable algorithms.

Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition-based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC’2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.

Conventionally, project managers schedule the activities first; later prepare for materials management, procurement, and supplier selection in project planning. This disjointed process leads in a loss of expected profit for the company owing to a lack of planning coordination. In this paper, a mixed integer programming model is developed for resource constrained multi-project scheduling, materials ordering (MO), and supplier selection (SS) problems to maximize the net profit of the organization. Since the resource constraint multi-project scheduling with MO and SS belongs to the class of problems that are NP-hard. Thus, a genetic algorithm-based memetic algorithm (MA) is proposed to solve the proposed model. The proposed MA utilizes the basic components of genetic algorithm (GA) like selection, crossover, and mutation with a local search. The proposed algorithm is tested on self-generated 16 instances with a range of 2 to 5 projects in a multi-project set with 30 to 90 activities in a project with 6 to 8 suppliers. The results revealed that the suggested MA generates better solutions than the GA by achieving projects higher net present value (NPV). The findings of this paper provide managerial insights for the project managers to secure organizations' profitability. [ABSTRACT FROM AUTHOR]

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions. The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype. Using any of the previous versions will give moderate results but with very low speed. Hence, a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols. The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques. Also, a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns. Various image samples with a wide range of scale, orientation, and illumination conditions have been experimented with to verify the effect of the new improvements. Encouraging results with 97.55% detection precision have been reported using the recent challenging public Chinese City Parking Dataset (CCPD) outperforming the author of the dataset by 3.05% and the state-of-the-art technique by 1.45%. [ABSTRACT FROM AUTHOR]

The quadratic minimum spanning tree problem (QMSTP) is a spanning tree optimization problem that considers the interaction cost between pairs of edges arising from a number of practical scenarios. This problem is NP-hard, and therefore there is not a known polynomial time approach to solve it. To find a close-to-optimal solution to the problem in a reasonable time, we present for the first time a clustering-enhanced memetic algorithm (CMA) that combines four components, i.e., (i) population initialization with clustering mechanism, (ii) a tabu-based nearby exploration phase to search nearby local optima in a restricted area, (iii) a three-parent combination operator to generate promising offspring solutions, and (iv) a mutation operator using Lévy distribution to prevent the population from premature. Computational experiments are carried on 36 benchmark instances from 3 standard sets, and the results show that the proposed algorithm is competitive with the state-of-the-art approaches. In particular, it reports improved upper bounds for the 25 most challenging instances with unproven optimal solutions, while matching the best-known results for all but 2 of the remaining instances. Additional analysis highlights the contribution of the clustering mechanism and combination operator to the performance of the algorithm. [ABSTRACT FROM AUTHOR]

The popularity of the short messaging services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adultthemed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. [ABSTRACT FROM AUTHOR]

This paper focuses on an energy-efficient distributed flexible flow shop scheduling problem (EEDFFSP) with variable machine speed. The EEDFFSP needs to solve four sub-problems: factory assignment, determination of the job sequence at each stage, machine selection, and the speed selection for each job on a machine. A multi-neighborhood-based multi-objective memetic algorithm (MMMA) is proposed to optimize total weighted tardiness and energy consumption. The MMMA employs a two-level encoding scheme including a job permutation and a speed matrix. A highly-efficient decoding strategy is utilized to reduce the search space of the sub-problems. In the initial phase, a weighted NEH (Nawaz-Enscore-Ham) based-initial method is developed to generate an initial population. Two genetic global search operators are designed to perform exploration evolution. Then, several multiple neighborhoods including several permutation adjustment operations within or between factories, an energy-saving strategy, and a speed adjustment strategy are integrated to enhance exploitation ability. The comprehensive experiments on extensive instances are performed to test the contribution of the main components and the performance of the MMMA. The average values of Hypervolume and Unary Epsilon indicators obtained by the variants of the MMMA without the initialization method, genetic global search, local search, and energy-saving strategy are worse than the complete MMMA, which demonstrates a significant contribution of these components to the MMMA. The MMMA obtains the best values of indicators among all the compared algorithms within a limited run time, which demonstrates the MMMA is an effective and efficient algorithm for solving the EEDFFSP. [ABSTRACT FROM AUTHOR]

When solving a scheduling problem, users are often interested in finding a schedule optimizing a given objective function. However, in some settings there can be hard constraints that make the problem unfeasible. In this paper we focus on the task of repairing infeasibility in job shop scheduling problems with a hard constraint on the makespan. In this context, earlier work addressed the problem of computing the largest subset of the jobs that can be scheduled within the makespan constraint. Herein, we face a more general weighted version of the problem, consisting in computing a feasible subset of jobs maximizing their weighted sum. To this aim, we propose an efficient memetic algorithm, that combines a genetic algorithm with a local search method, also proposed in the paper. The results from an experimental study show the practical suitability of our approach. [ABSTRACT FROM AUTHOR]

Quantum-inspired heuristic search algorithms have received widespread attention in solving the function optimal solution. However, the existing quantum search methods may lose their efficiency for high dimension function problems. To solve these problems, this study develops a novel memetic quantum optimization algorithm with levy flight (called MLQO) by exploring the principle of memetic computing and Levy flight. First, a memetic framework based on quantum mechanism is designed to balance the global search and the local search, which makes all particles gain a better experience from a local search. Second, an efficient local search strategy with levy flight is designed to enhance the searchability due to short walking distance and occasionally long jumps to be made by the particle. Furthermore, the lifetime of each particle is defined to determine whether a particle needs to be redistributed using the levy flight method in the search space. Numerical experiments are performed to evaluate the performance of the proposed algorithm by comparing it to other optimal algorithms. The results demonstrate the superiority of the proposed algorithm on well-known unimodal and multimodal benchmark functions. [ABSTRACT FROM AUTHOR]

Market impact costs are important factors to portfolio management, which always lead to adverse price fluctuations in trading. As the practical trading volume becomes increasingly large, the Problem of Portfolio Optimization with Market Impact Costs (MICPOP) has become more important. Traditional MICPOPs involve seeking an optimal allocation of capital to a limited number of assets with respect to either additional constraints or a weighted sum of objectives (net return, investment risk). We suggest solving MICPOPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate MICPOPs as a bi-objective optimization problem. The advantages of MOEAs over state-of-the-art single objective approaches to MICPOPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), fails to provide satisfactory solution quality sometimes. Hence, a memetic MOEA for Portfolio Optimization with Market Impact costs (POMI-MOEA), which inherits the global search capability of NSGA-II while introducing a new local search operator, is proposed and evaluated in this paper. Comprehensive experimental studies on 11 portfolio cases have shown the superiority of POMI-MOEA over NSGA-II and other two MOEAs for MICPOPs. [ABSTRACT FROM AUTHOR]