Multi-objective Artificial Bee Colony Algorithm Research Articles

Power Distribution Optimization Based on Demand Respond with Improved Multi-Objective Algorithm in Power System Planning

In this article, a novel dynamic economic load dispatch with emission based on a multi-objective model (MODEED) considering demand side management (DSM) is presented. Moreover, the investigation and evaluation of impacts of DSM for the next day are considered. In other words, the aim of economical load dispatch is the suitable and optimized planning for all power units considering different linear and non-linear constrains for power system and generators. In this model, different constrains such as losses of transformation network, impacts of valve-point, ramp-up and ramp-down, the balance of production and demand, the prohibited areas, and the limitations of production are considered as an optimization problem. The proposed model is solved by a novel modified multi-objective artificial bee colony algorithm (MOABC). In order to analyze the effects of DSM on the supply side, the proposed MODEED is evaluated on different scenarios with or without DSM. Indeed, the proposed MOABC algorithm tries to find an optimal solution for the existence function by assistance of crowding distance and Pareto theory. Crowding distance is a suitable criterion to estimate Pareto solutions. The proposed model is carried out on a six-unit test system, and the obtained numerical analyses are compared with the obtained results of other optimization methods. The obtained results of simulations that have been provided in the last section demonstrate the higher efficiency of the proposed optimization algorithm based on Pareto criterion. The main benefits of this algorithm are its fast convergence and searching based on circle movement. In addition, it is obvious from the obtained results that the proposed MODEED with DSM can present benefits for all consumers and generation companies.

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

The rapid growth of cloud computing in the last decade has led to an increasing concern about the energy requirement of cloud data centers. Dynamic virtual machine (VM) consolidation is an effective way to tackle this issue, where VMs are executed on as few physical machines (PMs) as possible. Meanwhile, VM placement must be performed strategically, by considering different factors of the available resources to optimal exploitation of them. Moreover, a major challenge is the system’s reliability degradation because of the high frequency of consolidation and placing VMs on unreliable PMs. In this paper, we address the problem by introducing a discrete-time Markov chain (DTMC) model to predict future resource usage. Using the DTMC model along with the reliability model of PMs leads to more accurate PMs categorization based on their status. Then, a multi-objective VM placement approach is proposed to achieve the optimal VMs to PMs mapping using the ε-dominance-based multi-objective artificial bee colony (ε-MOABC) algorithm which can efficiently balance the overall energy consumption, resource wastage, and the system reliability to meet SLA and QoS requirements. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using the CloudSim toolkit. Competitive analysis of the experimental results demonstrates that the proposed approach significantly improves energy consumption while avoiding the inefficient VM migrations.

A Novel Cost-Effective Controller Placement Scheme for Software-Defined Vehicular Networks

Energy costs have dramatically increased in data center networks as an increasing number of large-scale Internet applications are used. In software-defined vehicular networks (SDVN), the communication delay between two vehicles and between vehicles and the controller will dramatically climb up as the number of vehicles increases. This requires more controllers to provide communication service to minimize the latency. More controllers lead to high energy costs. Therefore, the number of controllers and their placement, the so-called controller placement problem (CPP), should be addressed. The appropriate placement of controllers can decrease the energy cost, enabling green communication in SDVN. Although CPP has been studied for static networks, it has not been effectively solved in highly dynamic and complex networks. In this article, a novel cost-effective CPP scheme for SDVN is proposed. First, our proposed minimum controller selection mechanism (MOSA) can reduce the number of controllers and guarantee the coverage of the area. Besides, an improved multiobjective artificial bee colony algorithm (IMABC) is proposed based on the original artificial bee colony algorithm. The IMABC can judge which controller should be switched on for data transmission based on real-time traffic flow. A route computation mechanism is proposed to evaluate the performance of our CPP scheme. The experimental results confirm that compared to other existing CPP schemes, our scheme can achieve a higher packet delivery ratio while greatly reducing energy consumption and latency.

Reliability-based equitable transit frequency design

Fairness is an important criterion for achieving sustainable urban development. While most existing studies focus on accessing or evaluating the fairness condition of a given transit network, this study explicitly incorporates fairness as an objective in the planning step. A multi-objective bilevel programming model is developed where the lower level problem is the reliability-based transit assignment problem and the upper level problem is to determine optimal frequency settings to simultaneously minimise the total effective travel cost and maximise the network fairness condition. A multi-objective Artificial Bee Colony algorithm is developed to solve the bilevel model. Numerical studies find that: (1) increasing the frequency may not improve the fairness condition; (2) there is a tradeoff between the two objectives (3) the effect of passengers’ risk aversion attitude on the fairness measurement depends on the frequency setting; it could either amplify the fairness measurements or have no impact.

Motion Pattern Optimization and Energy Analysis for Underwater Glider Based on the Multi-Objective Artificial Bee Colony Method

Underwater gliders are prevailing in oceanic observation nowadays for their flexible deployment and low cost. However, the limited onboard energy constrains their application, hence the motion pattern optimization and energy analysis are the key to maximizing the range of the glider while maintaining the acceptable navigation preciseness of the glider. In this work, a Multi-Objective Artificial Bee Colony (MOABC) algorithm is used to solve the constrained hybrid non-convex multi-objective optimization problem about range and accuracy of gliders in combination with specific glider dynamics models. The motion parameters Pareto front that balances the navigational index referring to range and preciseness are obtained, relevant gliding profile motion results are simulated simultaneously, and the results are compared with the conventional gliding patterns to examine the quality of the solution. Comparison shows that, with the utilization of the algorithm, glider voyage performance with respect to endurance and preciseness can be effectively improved.

Warehouse scheduling in airport freight station with multi-objective artificial bee colony algorithm

To improve the efficiency and safety of automated warehouse, a new scheme is proposed to solve the problem of space allocation considering path planning, different weight as well as the inbound and outbound frequency of cargoes. First of all, the storage location assignment problem was induced as a constraint multi-objective optimisation model whose aims are minimising the gravity centre of shelf and the frequency of inbound and outbound. Then, multi-objective artificial bee colony algorithm and multi-objective particle swarm optimisation algorithm were proposed and the performance was tested on the benchmark functions as well as the problem of storage space allocation. The results show that the proposed strategies effectively solve the problem of optimisation of storage space allocation and realise the efficient operation of the warehouse.

Warehouse scheduling in airport freight station with multi-objective artificial bee colony algorithm

To improve the efficiency and safety of automated warehouse, a new scheme is proposed to solve the problem of space allocation considering path planning, different weight as well as the inbound and outbound frequency of cargoes. First of all, the storage location assignment problem was induced as a constraint multi-objective optimisation model whose aims are minimising the gravity centre of shelf and the frequency of inbound and outbound. Then, multi-objective artificial bee colony algorithm and multi-objective particle swarm optimisation algorithm were proposed and the performance was tested on the benchmark functions as well as the problem of storage space allocation. The results show that the proposed strategies effectively solve the problem of optimisation of storage space allocation and realise the efficient operation of the warehouse.

A multi-strategy integrated multi-objective artificial bee colony for unsupervised band selection of hyperspectral images

Abstract As the spectral dimension of hyperspectral images increases, band selection becomes more and more important when using hyperspectral data. Evolutionary algorithms have been applied to the band selection problem of hyperspectral images, but most of existing methods focus only on single indicator, ignoring the whole characteristics of hyperspectral image. In this paper we study a multi-objective artificial bee colony approach for the band selection problem of hyperspectral images. Firstly, a new multi-objective unsupervised band selection model is proposed by using both band correlation and information amount. Secondly, a multi-strategy integrated multi-objective artificial bee colony algorithm (MABC-BS) is proposed to deal with the band selection model above. Several new operators, including the multi-direction search strategy, the x-space crowing degree-based search strategy, and the adaptive mutation, are developed to enhance the proposed algorithm. Compared with eight representative algorithms on three typical test problems, experimental results on the classification performance of four classifiers (i.e., Random Forest, SVM, KNN, ObRaF) show that the proposed algorithm is a powerful approach for tackling the problem of hyperspectral band selection.

Energy‐aware dynamic‐link load balancing method for a software‐defined network using a multi‐objective artificial bee colony algorithm and genetic operators

Information and communication technology (ICT) is one of the sectors that have the highest energy consumption worldwide. It implies that the use of energy in the ICT must be controlled. A software-defined network (SDN) is a new technology in computer networking. It separates the control and data planes to make networks more programmable and flexible. To obtain maximum scalability and robustness, load balancing is essential. The SDN controller has full knowledge of the network. It can perform load balancing efficiently. Link congestion causes some problems such as long transmission delay and increased queueing time. To overcome this obstacle, the link load balancing strategy is useful. The link load-balancing problem has the nature of NP-complete; therefore, it can be solved using a meta-heuristic approach. In this study, a novel energy-aware dynamic routing method is proposed to solve the link load-balancing problem while reducing power consumption using the multi-objective artificial bee colony algorithm and genetic operators. The simulation results have shown that the proposed scheme has improved packet loss rate, round trip time and jitter metrics compared with the basic ant colony, genetic-ant colony optimisation, and round-robin methods. Moreover, it has reduced energy consumption.

Development of Thermal Design for Predetermined Temperature Distribution Based on Combination with Reduced Order Model and Multi-Objective Artificial Bee Colony Algorithm

Development of Thermal Design for Predetermined Temperature Distribution Based on Combination with Reduced Order Model and Multi-Objective Artificial Bee Colony Algorithm

Pioneer Pareto artificial bee colony algorithm for three-dimensional objective space optimization of composite-based layered radar absorber

A three-dimensional objective space (3DOS) optimization strategy using an enhanced multi-objective artificial bee colony (ABC) algorithm for the design optimization of layered radar absorbing material (LRAM) is presented in this study. The multi-objective exploitation ability of ABC is improved with regard to the convergence and diversity by integrating a pioneer Pareto (PP) solution to the onlooker bee phase, which is selected from the Pareto optimal set. Initially, the performance of PP-ABC is successfully verified by a comparison with ABC and the well-known multi-objective counterparts like particle swarm optimization (PSO) and differential evolution (DE) algorithms. The comparison is carried out through five multi-objective benchmark functions with respect to three favorable and reliable multi-objective indicators such as hypervolume (HV), HV ratio and Pareto sets proximity (PSP). The employed three objective functions to be the dimensions of 3DOS are weighted bandwidth-based total reflection coefficient involving sub-reflection waves of a wide oblique incident angular range 0°–75°, the total thickness and the number of layers. By using PP-ABC, a 3D designed LRAM operating at a large frequency band of 2–18 GHz is then designed for synchronously minimizing the three objective vectors by finding out the design variables: thickness and material types. Meanwhile, the material types of the proposed LRAM are optimally picked up from a composite material database with 51 specimens from 9 previously reported studies (51/9#database). In order to point out the effectiveness of the proposed 3DOS optimization strategy, three LRAMs are also compared with respective reported designs whose material type is selected from a database with 6 specimens (6/1#database). The results show that the proposed LRAMs are hence the global optimal designs in terms of all objective functions thanks to the proposed 3DOS optimization strategy based on PP-ABC.

Multiobjective Modeling and Optimization for Scheduling a Stochastic Hybrid Flow Shop With Maximizing Processing Quality and Minimizing Total Tardiness

Currently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs' processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem.

Design and optimisation of an advanced waste heat cascade utilisation system for a large marine diesel engine

Growing energy shortages severely restricting the sustainable development of human beings, improving energy efficiency has become an urgent issue. To improve energy efficiency, the dual-pressure organic Rankine cycle, which is an efficient waste heat recovery technique for ships, has received increasing attention. Most researchers have investigated the dual-pressure organic Rankine cycle with pure working fluids, which do not yield the best thermodynamic and economic performance. In addition, some low-grade waste heat sources in the engine have been neglected. To completely recover the waste heat energy of marine engines, a novel waste heat recovery technique with a dual-pressure organic Rankine cycle system, desalination plant, and two-bed regenerative adsorption refrigeration device is presented for recovering residual heat from the exhaust gas, jacket water, charge air, and lubricating oil. Based on a comparison of the thermodynamic performance, safety, and environmental effects, the zeotropic working fluid pair dimethyl carbonate/perfluoropropane is selected. The mathematical model of the proposed integrated system is evaluated to ensure accuracy, and its critical parameters are analysed based on its thermodynamic and economic performance. A multi-objective artificial bee colony algorithm is adopted to optimise the system. The results show that the proposed integrated system can recover 354.65 kW net power and increase the thermal efficiency by 4.2% on average; at the most frequently used engine load, a refrigeration capacity of 88 kW and a fresh water rate of 146 kg/h are achieved. This work promotes the development of the energy recycling.

Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

Retrieval of optimal solution(s) for a permutation flowshop scheduling problem within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criterions like makespan, total flowtime, earliness, tardiness, etc and obtaining a Pareto solution for final decision making. This paper remodels a discrete artificial bee colony algorithm for permutation flowshop scheduling problem executed through three different scenarios raised the analysis of time complexity measure. To enhance the search procedure, we have explored the alternative and combined use of two local search algorithms named as: iterated greedy search algorithm and iterated local search algorithm in our discrete artificial bee colony algorithm and the results are summarized with respect to completion time, mean weighted tardiness, and mean weighted earliness. The two algorithms are prioritised on insertion and swap of neighbourhood structures which will intensify the local optima in the search space. Further the performance of the algorithm is compared with the test results of multi-objective artificial bee colony algorithm. The result of our optimization process concludes with a set of non-dominated solutions lead to different Pareto fronts. Finally, we propose a chaotic based technique for order of preference by similarity to ideal solution (chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-objective problem domain.

Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms

Robots are extensively used during the era of Industry 4.0 to achieve high productivity, better quality and lower cost. While designing a robotic assembly line, production managers are concerned about the cost involved in such a system development. Most of the research reported till date did not consider purchasing cost while optimizing the design of a robotic assembly line. This study presents the first attempt to study the cost-oriented robotic assembly line balancing problem with setup times to minimize the cycle time and total purchasing cost simultaneously. A mixed-integer linear programming model is developed to formulate this problem. The elitist non-dominated sorting genetic algorithm (NSGA-II) and improved multi-objective artificial bee colony (IMABC) algorithm are developed to achieve a set of Pareto solutions for the production managers to utilize for selecting the better design solution. The proposed IMABC develops new employed bee phase and scout phase, which selects one solution in the permanent Pareto archive to replace the abandoned solution, to enhance exploration and exploitation. The comparative study on a set of generated instances demonstrates that the proposed model is capable of achieving the proper tradeoff between line efficiency and purchasing cost, and the proposed NSGA-II and IMABC achieve competing performance in comparison with several other multi-objective algorithms.

A Multiobjective Disassembly Planning for Value Recovery and Energy Conservation From End-of-Life Products

Demanufacturing aims to recover value and conserve energy from end-of-life (EOL) products, contributing to sustainable manufacturing. To make the full use of EOL products, they are usually disassembled into components that have different values and embodied energy at different EOL options. This article studies a disassembly planning (DP) that integrates the decisions on disassembly sequence and EOL strategy to maximize the recovered value and energy conservation from EOL products. We propose a multiobjective DP based on the value recovery and energy conservation (MDPVE) model, which is different from the existing DP models by focusing on the embodied energy rather than the energy consumption during disassembly. An adapted multiobjective artificial bee colony (ABC) algorithm [multiobjective ABC (MOABC)] is developed to identify the Pareto solutions for the MDPVE and is compared with a well-known metaheuristic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II). A real-world case study demonstrated the superior solution quality and computational efficiency of MOABC. Note to Practitioners-There is often more than one treatment option for EOL products or components, including reuse, remanufacturing, and recycling. However, the decision on which EOL option to select is not considered in most of the DP studies by assuming an EOL option given for each component. Hence, the disassembly plan with the EOL decision is focused in this article. As energy sustainability gains an increasing attention, it is essential to assess the profitability and energy conservation simultaneously for EOL products. Since there could be a tradeoff between recovered profit and conserved energy, a multiobjective evolutionary algorithm is developed for generating Pareto solutions which help decision-makers to find good solutions for both evaluation indicators.

A decomposition-based multi-objective optimization approach for extractive multi-document text summarization

Currently, due to the overflow of textual information on the Internet, automatic text summarization methods are becoming increasingly important in many fields of knowledge. Extractive multi-document text summarization approaches are intended to automatically generate summaries from a document collection, covering the main content and avoiding redundant information. These approaches can be addressed through optimization techniques. In the scientific literature, most of them are single-objective optimization approaches, but recently multi-objective approaches have been developed and they have improved the single-objective existing results. In addition, in the field of multi-objective optimization, decomposition-based approaches are being successfully applied increasingly. For this reason, a Multi-Objective Artificial Bee Colony algorithm based on Decomposition (MOABC/D) is proposed to solve the extractive multi-document text summarization problem. An asynchronous parallel design of MOABC/D algorithm has been implemented in order to take advantage of multi-core architectures. Experiments have been carried out with Document Understanding Conferences (DUC) datasets, and the results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The obtained results have improved the existing ones in the scientific literature for ROUGE-1, ROUGE-2, and ROUGE-L scores, also reporting a very good speedup.

Evaluation Method of Multiobjective Functions' Combination and Its Application in Hydrological Model Evaluation.

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions' combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions' combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

Multi-objective artificial bee colony algorithm for multi-stage resource leveling problem in sharing logistics network

Multi-stage resource leveling problem in sharing logistics network is a multi-objective optimization problem, which is strongly non-deterministic polynomial hard in open loop environment. In this paper, we attempt to develop a logistics task-resource allocation model for this problem, which not only considers the total cost and duration for sharing logistics network, but also refers to the resource efficiency intra-stage and stability inter-stage for resource providers. As the defects of slow convergence, weak local search and easy-to-precocious in traditional algorithms, an improved multi-objective artificial bee colony algorithm is developed with adaptive neighborhood rules. The process of algorithm improvement involves: (I) an adaptive moving step size in population update strategy instead of random step size and (II) an adaptive weigh updating method with multiple neighborhood search rules in local optimum. The results show that the improved algorithm can effectively solve multi-stage resource leveling problem proposed in this paper, compared with traditional artificial bee colony algorithm, non-dominated sorting genetic algorithm-II and multi-objective particle swarm optimization, and can obtain a better non-dominated solution set with multiple metrics for algorithm.

Parallel multi-objective artificial bee colony algorithm for software requirement optimization

In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development. Usually, due to lack of funds, lack of time and dependency between requirements, there is no possibility to develop all the required requirements. There are two conflicting objectives for choosing an optimal subset of the requirements: increasing customer satisfaction and reducing development costs. This problem is known as the next release problem (NRP) and is categorized as an NP-hard problem. Unlike the standard version of the NRP, we formulate this problem as a restricted multi-objective optimization problem. There exist metaheuristic algorithms for solving this problem performed as serials. In this paper, we introduce a parallel algorithm based on the master–slave model in order to improve the quality of the solutions. Based on the criteria of multi-objective problems, the quality of the obtained solution is compared with several metaheuristic algorithms. Two scenarios and two different datasets are used for experiments. Results indicate that the proposed method in the first scenario would highly improve the quality of solutions. Moreover, the method reduces execution time significantly through improvement in the quality of the solution in the second scenario.