Single-objective Algorithm Research Articles

Malicious Code Detection under 5G HetNets Based on a Multi-Objective RBM Model

The fifth generation (5G) mobile communication technology brings people a higher perceived rate experience, the high-quality service of high-density user connection, and other commercial applications. As an important means of data processing in 5G heterogeneous networks (HetNets), data fusion technology is faced with a large number of malicious code attacks. Thus, it is particularly important to find an efficient malicious code detection method. However, in the traditional research, due to dataset imbalance, the complexity of the deep learning network model, the use of a single-objective algorithm, and other factors, it brings greater loss and lower detection accuracy. Therefore, how to choose a suitable network model and improve the data classification accuracy in HetNets is a big challenge. To enhance the model's robustness, a multi-objective restricted Boltzmann machine (RBM) model is designed for training. In this article, evaluation indices are used to comprehensively measure the effect of data classification, introducing a strategy pool to improve the effect of data fusion and using non-dominated sorting genetic algorithms (NSGA-II) to deal with the imbalanced malware family. Experimental results demonstrate that the proposed multi-objective RBM model combined with NSGA-II can effectively enhance the data classification accuracy of HetNets and reduce the loss in the process of data fusion.

Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers

Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider’s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms.

REVIEW OF THE MULTI-OBJECTIVE SWARM INTELLIGENCE OPTIMIZATION ALGORITHMS

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results show that several MOO approaches have not been used in most of the proposed MOSI algorithms. Integrating other different MOO approaches may help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms have been evaluated using MOPs with two objectives, which clarifies open issues in this research area.

Multivariate Optimization in Large-Scale Building Problems: An Architectural and Urban Design Approach for Balancing Social, Environmental, and Economic Sustainability

This paper explores the promise of genetic algorithms as a tool for optimization of buildings at a neighborhood scale across the conflicting demands of social, environmental, and economic sustainability. A large urban site in Chicago, Illinois, is selected to test the viability of using a multi criteria genetic algorithm to optimize the potential building mix in a newly planned development. Two variables, the number of buildings of a given use-type and their height, are analyzed against cost functions for social, economic, and environmental objectives. Single-objective algorithms are utilized to optimize the variables individually. A non-dominated genetic sorting algorithm (NGSAII) is then utilized to identify the Pareto-optimal solutions considering the three objectives simultaneously. Single-objective results are found to vary substantially by objective, with different variable values for social, economic, and environmental sustainability. For multi-objective algorithms, the results support Campbell’s notion of the three nodes of sustainability being in conflict. Solutions performing well across economic and environmental objectives were most common. Solutions performing well among environmental and social objectives were less common. Solutions performing well across economic and social performance were rare. This suggests that while economic and environmental conflicts are to some degree resolvable, conflicts between social and either of economic or environmental performance are more difficult to resolve. The failure of any solution to perform well across all three objectives lends credence to the idea of design as a series of trade-offs and that one super optimum solution may not exist. The process provides insights into the trade-offs implicit in the building design and development process and raises questions regarding the balancing of competing sustainability objectives.

Improved NSGA-II for the minimum constraint removal problem

This paper proposes a comprehensive multi-objective evaluation model to derive a feasible solution to MCR path planning that is from the robot's individual perspective, is driven by the costs and benefits, and takes into account factors such as the minimum constraint set, the route length, and the cost. The feasible solution to the MCR path is evaluated using this model. A typical multi-objective algorithm NSGA-II is applied to the MCR problem. The algorithm test and real scenario test results show that compared with single objective algorithm planning, the NSGA-II-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform the MCR path planning more effectively.

Solving multitrip pickup and delivery problem with time windows and manpower planning using multiobjective algorithms

The multitrip pickup and delivery problem with time windows and manpower planning ( MTPDPTW-MP ) determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP ( MO-MTPDPTWMP ) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection ( MOILS-ANS ) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

Multiobjective Particle Swarm Optimization Based on Network Embedding for Complex Network Community Detection

Community detection in complex networks is significant to social network analysis. Most of the algorithms take advantage of single-objective optimization methods, which may not be effective for complex networks. Compared with single-objective algorithms, multiobjective evolutionary algorithms can avoid local optimization. However, multiobjective evolutionary algorithms often encounter problems of excessive search space and low efficiency. To solve these issues, this study introduces network embedding into the multiobjective particle swarm algorithm and maps nodes into a low-latitude space, thereby effectively reducing the search space while increasing search efficiency via a consensus propagation strategy. Experimental results demonstrate that a novel effective algorithm based on multiobjective particle swarm optimization (NE-PSO) performs effectively and has competitive performance in comparison with state-of-the-art approaches on synthetic and real-world networks, especially the large-scale ones.

A beam search algorithm for the biobjective container loading problem

In this work we deal with a biobjective variant of the single container loading problem. The problem consists in packing a set of boxes into a large box or container. In addition to the usual objective related to maximize the volume utilization, we include a second objective consisting on maximizing the total profit (e.g., weight or priority) of the loaded boxes. We propose to adapt bsg, a state-of-the-art single-objective algorithm for solving the biobjective problem. bsg can be seen as an incomplete version of a breadth first search in which each level of the search tree is restricted to a fixed number of promising nodes. In order to select the most promising nodes, the algorithm constructs a solution from them by using a greedy algorithm and evaluates the objectives. To deal with two objectives, we propose to select nodes by using well-known multi-objective criteria based on dominance and crowding distance related to the greedy evaluations. We go a little bit further and orient the greedy algorithm to one objective or the other by using a mechanism which changes the configuration of the heuristic function dynamically. The proposal is compared with bsg in a set of well-known instances reporting a 24% increase in the hypervolume indicator. Compared to other biobjective approaches, the reported results are also promising. Codes and test instances are available in: https://github.com/rilianx/Metasolver#bo-bsg

Reentrant hybrid flow shop scheduling problem with non-anticipatory sequence-dependent setup time

This paper studies a hybrid flow shop scheduling problem originated from a steam turbine rotor production. Reentrance and non-anticipatory sequence-dependent setup time are considered. The objectives are to minimise makespan and total tardiness. A mixed integer linear programming model is formulated. A bi-objective simulated annealing algorithm is developed to solve problems with real size. A modified version of the Nawaz-Enscore-Ham algorithm is applied to find a good initial solution and a Pareto search mechanism is introduced to find the Pareto set. The proposed bi-objective simulated annealing algorithm is compared with a single-objective algorithm. Experimental results demonstrate the superiority of the bi-objective algorithm. Sensitivity analysis provides valuable managerial insights for real production.

Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

Feature selection aims to confiscate inappropriate features and yet improve classification performance. These aims are conflicting with one another, and a choice must be made in the presence of the trade-off between them. Numerous researches deal with feature selection problem but, they are mostly single-objective based. Nowadays, multi-objective optimisation approaches are becoming the most suitable approaches to deal with feature selection problems. They can easily create a balance between selected features and classification accuracy or error rate. Evolutionary computation techniques have been applied for multi-objective feature selection. Cuckoo optimisation algorithm is among the most popular technique that is exceptional in solving the problems of feature selection. Based on the binary cuckoo optimisation algorithm, two different multi-objective filter-based feature selection frameworks are presented with the idea of nondominated sorting genetic algorithms NSGAIII (BCNSG3) along with NSGAII (BCNSG2). Thus, four multi-objective filter-based feature selection approaches are proposed by employing mutual information along with gain ratio based-entropy as the respective filter evaluation measures in all the proposed frameworks. The results obtained are examined and analysed against the existing methods and single objective scheme on fourteen (14) datasets of varying degree of difficulties. The outcome of the experiments displays that the proposed multi-objective algorithms successfully derive a set of nondominated solutions that used the least feature size and attained the best error rate than using full-length features. In general, BCNSG2 obtained the best results compared to the existing methods and single-objective algorithm, whereas BCNSG3 outdoes all other approaches.

Multiobjective optimization of smart grids considering market power

Smart grids gain acceptance for promoting the efficient use of energy resources, based on market prices. These include energy storage systems and electric vehicles; in terms of operation they are complex for controlling the loading / unloading of energy or the buy / sell of it. These networks also encourage demand response programs, that is, according to the price, the users decide how much energy they consume. In addition to promoting the use of renewable energy. This research presents two contributions: 1) The implementation of market power indicators to a mathematical model of smart microgrid and 2) The implementation of a new multiobjective hybrid algorithm called “variable neighborhood search: the differential evolutionary particle swarm”. The results are close to the Pareto front with a uniform distribution. Then, the smart microgrid is evaluated with two restrictions: the Herfindahl-Hirschman index and the three biggest bidders’ index, the first contribution allows no bidder to exercise market power during the 24 hours, which guarantees a competitive electricity market. The second contribution consists in converting this single objective algorithm to a multiobjective version. The performance of the new multiobjective algorithm is verified with the test problems showing good performance.

Reentrant hybrid flow shop scheduling problem with non-anticipatory sequence-dependent setup time

This paper studies a hybrid flow shop scheduling problem originated from a steam turbine rotor production. Reentrance and non-anticipatory sequence-dependent setup time are considered. The objectives are to minimise makespan and total tardiness. A mixed integer linear programming model is formulated. A bi-objective simulated annealing algorithm is developed to solve problems with real size. A modified version of the Nawaz-Enscore-Ham algorithm is applied to find a good initial solution and a Pareto search mechanism is introduced to find the Pareto set. The proposed bi-objective simulated annealing algorithm is compared with a single-objective algorithm. Experimental results demonstrate the superiority of the bi-objective algorithm. Sensitivity analysis provides valuable managerial insights for real production.

Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining

Association rules mining (ARM) is one of the most popular tasks of data mining. Although there are many effective algorithms run on binary or discrete-valued data for the problem of ARM, these algorithms cannot run efficiently on data that have numeric-valued attributes. However, in many real-world applications, the data usually consist of numerical values. It is a difficult problem to determine which attributes will be included in the discovered rules; automatically adjust the ranges of the attributes in the most appropriate way; rapidly discover the reduced high-quality rules directly without generating the frequent itemsets ensuring the rules to be comprehensible, surprising, interesting, accurate, and confidential. Furthermore, adjusting all these processes without the need for metrics to be determined a priori for each data set is of great importance in terms of automating this problem. Recently, numerical ARM has been dealt with as a multi-objective problem that best meets different criteria at the same time. In this study, algorithms which consider numerical ARM as a multi-objective optimization problem were examined and the performance analysis of these algorithms was performed for the first time to the best of our knowledge. A comparative analysis of MOPNAR, QAR-CIP-NSGA II, NICGAR, MODENAR, MOEA_Ghosh, and ARMMGA methods in terms of the number of rules, average support, average confidence, average lift, average conviction, average certain factor, average netconf, average yulesQ, and coverage percentage metrics in the real-world data consisting of numerical attributes was performed. The performances these algorithms were tested with single-objective optimization methods for ARM in this study. It is found that MOEA-Ghosh is the most effective multi-objective method in terms of average support and average confidence measures in data sets containing high number records and attributes. The best results in terms of average support value were obtained by MOEA-Ghosh algorithm and the average confidence values were obtained by multi-objective QAR-CIP-NSGAII in data sets containing relatively few records and attributes. Furthermore, it can be concluded that multi-objective algorithms outperformed the single-objective algorithms with respect to average support, lift, certain factor, netconf, and yulesQ metrics.

Multi-objective clustering: a kernel based approach using Differential Evolution

ABSTRACTA multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering.

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

Privacy-preserving data mining (PPDM) has become an interesting and emerging topic in recent years because it helps hide confidential information, while allowing useful knowledge to be discovered at the same time. Data sanitization is a common way to perturb a database, and thus sensitive or confidential information can be hidden. PPDM is not a trivial task and can be concerned an Non-deterministic Polynomial-time (NP)-hard problem. Many algorithms have been studied to derive optimal solutions using the evolutionary process, although most are based on straightforward or single-objective methods used to discover the candidate transactions/items for sanitization. In this paper, we present a multi-objective algorithm using a grid-based method (called GMPSO) to find optimal solutions as candidates for sanitization. The designed GMPSO uses two strategies for updating gbest and pbest during the evolutionary process. Moreover, the pre-large concept is adapted herein to speed up the evolutionary process, and thus multiple database scans during each evolutionary process can be reduced. From the designed GMPSO, multiple Pareto solutions rather than single-objective algorithms can be derived based on Pareto dominance. In addition, the side effects of the sanitization process can be significantly reduced. Experiments have shown that the designed GMPSO achieves better side effects than the previous single-objective algorithm and the NSGA-II-based approach, and the pre-large concept can also help with speeding up the computational cost compared to the NSGA-II-based algorithm.

A Gaussian process regression based on variable parameters fuzzy dominance genetic algorithm for B-TFPMM torque estimation

Transverse Flux Permanent Magnet Motor (TFPMM) has received extensive attention in the field of electric vehicles. The magnetic circuit of TFPMM is three-dimensional and non-linear, which leads to the high nonlinearity of electromagnetic torque. Although the three-dimensional finite element method (3DFEM) could be used to estimate torque, it is very time-consuming. Instead of it, this study adopts a kind of machine learning method—Gaussian process regression (GPR). For the hyper-parameters optimization of GPR, most previous studies used single-objective algorithms for improving the regression accuracy. However, GPR is an algorithm of probability prediction and it could not guarantee to have the satisfactory confidence interval characteristic simultaneously while the regression precision achieves optimal. Therefore this paper proposes a variable parameters fuzzy dominance genetic algorithm (VPFDGA) which is suitable for the multi-objective optimization, including the optimization of regression precision, confidence interval reliability, confidence interval width and skill score. By combining GPR with VPFDGA, the electromagnetic torque of a building-block transverse flux permanent magnet motor (B-TFPMM) is estimated by VPFDGA-GPR (GPR based on variable parameters fuzzy dominance genetic algorithm). Besides, two other GPRs based multi-objective optimization, three GPRs based on single-objective optimization and a GPR based on weighted sum method that is the classic multi-objective optimization algorithm are all implemented to compare with VPFDGA-GPR. The results of comparison show that VPFDGA-GPR has the better performances including the higher regression precision, more powerful ability of probability prediction, higher stability, less convergence time and so on.

A survey on partitional clustering using single-objective metaheuristic approach

Clustering is one of the important functions of data mining, which is used to analyse a large amount of data. It groups these set of data according to some similarity property such that data within the cluster are similar to each other and data between the clusters are dissimilar to each other. To obtain an optimal clustering result with the help of an optimisation algorithm is an emerging trend in data mining. The partitional clustering is one of the popularly used types of clustering algorithm. These algorithms often land in local optimum and number of clusters needs to be predefined. To encounter the above problem, optimisation algorithms such as metaheuristic algorithms are used as a suitable problem-solving paradigm. This paper presents an overview of single-objective metaheuristic algorithms used for partitional clustering problem and their applications. This paper even presents the research issues which can be dealt with in future.

Stochastic Modeling and Assisted History-Matching Using Multiple Techniques of Multi-Phase Flowback from Multi-Fractured Horizontal Tight Oil Wells

In this paper, the methods developed by [1] are used to analyze flowback data, which involves modeling flow both before and after the breakthrough of formation fluids. Despite the versatility of these techniques, achieving an optimal combination of parameters is often difficult with a single deterministic analysis. Because of the uncertainty in key model parameters, this problem is an ideal candidate for uncertainty quantification and advanced assisted history-matching techniques, including Monte Carlo (MC) simulation and genetic algorithms (GAs) amongst others. MC simulation, for example, can be used for both the purpose of assisted history-matching and uncertainty quantification of key fracture parameters. In this work, several techniques are tested including both single-objective (SO) and multi-objective (MO) algorithms for history-matching and uncertainty quantification, using a light tight oil (LTO) field case. The results of this analysis suggest that many different algorithms can be used to achieve similar optimization results, making these viable methods for developing an optimal set of key uncertain fracture parameters. An indication of uncertainty can also be achieved, which assists in understanding the range of parameters which can be used to successfully match the flowback data.

Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems.

Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective optimisation problems, the bi-objective permutation flowshop and travelling salesman problems. Our results show that multi-objective algorithms are indeed best configured using a multi-objective configurator.

A branch-and-bound method for the bi-objective simple line assembly balancing problem

The design of a production system is a strategic level decision. One of the key problems to solve is the line balancing problem that determines the efficiency of a production or assembly line. This class of problem has been widely studied in the literature. It determines important features, such as the number of stations, the takt time or the working conditions. Most of the variants of this problem consider only one objective function, but nowadays companies have to take into account different criteria. In this study, we consider a bi-objective variant of the simple assembly line balancing problem. We present a generic branch-and-bound method to solve exactly this problem. The objective functions are to minimise the takt time and the number of stations. To do so, bounds and bound sets are developed. The resulting method is numerically tested and compared to an ϵ-constraint method. These experiments show that the bi-objective branch-and-bound algorithm outperforms an ϵ-constraint method using a state-of-the-art single objective algorithm for more than 80% of the instances. Finally, we propose an analysis of the cases where the branch-and-bound method is outperformed.