Hybrid Multi-objective Evolutionary Algorithm Research Articles

A hybrid multi-objective evolutionary algorithm to integrate optimization of the production scheduling and imperfect cutting tool maintenance considering total energy consumption

With the increase in awareness of energy conservation and emission reduction in the manufacturing sector, the energy efficiency optimization of the production process is considered an effective way to promote cleaner production and environmentally sustainable development. Moreover, the transportation process and its energy consumption are non-negligible during the entire workshop production process. In practice, cutting tool degradation during operation is inevitable, which causes the actual processing time of work to be extended, so it is necessary to consider the degradation effects and imperfect maintenance. To reduce the total energy consumption in the manufacturing workshop with degradation effects and imperfect maintenance, this paper constructs a novel integrated optimization model including the flexible job-shop scheduling problem (FJSP), forklift transportation and imperfect cutting tool maintenance. First, an imperfect cutting tool maintenance with degradation effect strategy is presented, which makes the actual processing time of each operation more closely approximate the real-world processing situation. Second, an integrated multi-objective optimization model is formulated to simultaneously minimize the makespan, total tardiness, total production cost and total energy consumption. Third, a hybrid multi-objective evolutionary algorithm with Pareto elite storage strategy (HMOEA/P) is proposed to address the model. More precisely, three improved operations are presented: hybrid dominance principle, local search algorithms and Pareto elite preservation strategy. The effectiveness and feasibility of the parameter setting, improved operations and the proposed algorithm are separately demonstrated by the comparative experiments. Last, the superiority of the integrated multi-objective optimization model is proven by comparing different maintenance strategies and energy consumption models.

A Variable Neighborhood Search-Based Hybrid Multiobjective Evolutionary Algorithm for HazMat Heterogeneous Vehicle Routing Problem With Time Windows

Heterogeneous vehicle routing problem with time windows (HVRPTW) aspect of hazardous material (HazMat) transportation is studied in this article. Given the multiobjective nature of HazMat transportation, a three-objective optimization model is defined for HVRPTW in HazMat transportation. The objective is to determine fleet size and routes so as to meet all given constraints as well as to minimize the objectives of the total traveling cost, the transportation risk, and the average vehicle redundancy. A load-variant HazMat transportation risk assessment model considering vehicle type and waiting time is presented to describe the transportation risk. A variable neighborhood search-based hybrid multiobjective evolutionary algorithm (VN-HMOEA) is proposed for solving the problem. The proposed VN-HMOEA integrates a two-phase push forward insertion heuristic (TP-PFIH) for initial population construction, specialized evolutionary operators for optimizing different objectives and a VNS metaheuristic for local search exploitation. The algorithm is tested on the modified Solomon benchmark instances for HVRPTW. Experimental results show that the proposed VN-HMOEA is competitive in terms of convergence and diversity. We also find that multiobjective fashion is of great significance for transportation risk mitigation to provide a set of nondominated solution rather than a single solution.

Solving Multi-Objective Master Production Scheduling Model of Kalak Refinery System Using Hybrid Evolutionary Imperialist Competitive Algorithm

The improvement of operational planning in the field of oil refinery management is becoming increasingly essential and valid. The influential primary factor, among others, is the ever-changing economic climate. The industry must continually assess the potential impacts of variations in the final product demand, price fluctuations, crude oil compositions and even seek out immediate opportunities within the market. The Master Production Schedule (MPS) is a planned process within the Production Management System that provides a mechanism for active collaboration between the marketing and manufacturing processes. However, the problem of MPS is a predictable non-deterministic, polynomial-time and NP-hard combination optimisation issue. The global search for the best solution to the MPS problem involves determination and funds that many industries are reluctant to provide. Hence, the alternative approach using meta-heuristics could provide desirable and workable answers in a realistic computing period. In this paper, a unique hybrid Multi-Objective Evolutionary Imperialist Competitive Algorithm (MOEICA) is proposed. The algorithm combines the advantages of an Imperialist Competitive Algorithm (ICA) and a Genetic Algorithm (GA) to optimise a multi-objective master production schedule (MOMPS). The primary objective is to integrate the ICA with GA operators. The paper will also apply the optimised MOMPS to the Kalak Refinery System (KRS) operations using the proposed algorithm. The application involves determining the available capacity of each production line by estimating the parametric values for all failures. In addition, the gross requirements using demand forecasting and neural networks are defined. The proposed algorithm proved efficient in resolving the issues of the MOMPS model within KRS compared to the NSGAII and MOPSO algorithms. The results reflect that the novel MOEICA algorithm outperformed NSGAII and MOPSO in almost all measurements.

A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem

A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem.

Hybrid Multi-Objective Evolutionary (H-MOE) Algorithm for Solving RALB-II Problem

In this paper, we propose an MIP model for minimisation of cycle time and total assembly line cost simultaneously. Due to NP-hard nature of RALB (Rubinovitz and Bukchin, 1991), and to avoid local minima, a hybrid multi-objective evolutionary (H-MOE) algorithm developed based on the features of NSGA-II and simulated annealing algorithm is used to solve the RALB-II problem. Performance of the proposed algorithm is evaluated using datasets from Mukund et al. (2017b) and it was found that H-MOE algorithm outperformed the algorithm by Mukund et al. (2017b) in five out of seven cases on saving in cycle time and four out of seven in terms of total cost saving. In terms of average improvement, the proposed algorithm outperformed in terms total cost saving and underperformed in terms of time cycle compared with the performance of algorithm by Mukund et al. (2017b). Conclusions and future scope are highlighted.

Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW

The vehicle routing problem with time windows (VRPTW) is an important and widely studied combinatorial optimization problems. This paper aims at VRPTW with the objectives of reducing the number of vehicles and minimizing the time-wasting during the delivery process caused by early arrival. To solve this NP-hard problem, a hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search (HMOEA-GL) is proposed. Firstly, fast sampling strategy-based global search (FSS-GS) of HMOEA-GL extensively explores the entire solution space to quickly guide the search direction towards the center and edge areas of Pareto frontier. Secondly, route sequence difference-based local search (RSD-LS) is executed on the individuals with poor performance in the population obtained by FSS-GS to enhance the search ability of HMOEA-GL. In addition, the suitable coding method and proper genetic operators are designed, especially, a simple insertion search is used to reduce the number of vehicles in VRPTW. Comparing with NSGA-II, SPEA2, and MOEA/D, experimental results on 12 Solomon benchmark test problems indicate that the proposed HMOEA-GL is effective, and more excellent in convergence, while maintaining a satisfying distribution performance. HMOEA-GL could be an effective intelligent algorithm for expert and intelligent decision support system to help logistics companies to make decisions.

Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems

Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems.

A novel Hybrid Multi-objective Evolutionary Algorithm for the bi-Objective Minimum Diameter-Cost Spanning Tree (bi-MDCST) problem

Given a connected, weighted, undirected graph G = (V, E), the bi-objective Minimum Diameter-Cost Spanning Tree (bi-MDCST) Problem aims to find spanning trees on G with minimal total edge weight as well as minimal diameter. The problem is known to be NP-hard and finds application in domains ranging from transportation to network design. An exact algorithm for the problem is given in the literature, but is computationally very expensive and hence applicable to only very small graphs; some fast heuristics, a multi-objective genetic algorithm (MOGA) and a fast non-dominated sorting genetic algorithm (NSGA2) have also been proposed in the literature for solving the problem and their performance studied on larger sized full graphs containing up to 250 vertices and 31,125 edges. This paper presents a novel Hybrid Multi-objective Evolutionary Algorithm (HMOEA) for the bi-MDCST problem which uses a representation that captures the genetic information of several spanning trees of varying diameters in a single chromosome, thereby enhancing the representational power of the population. The algorithm uses heuristic-based population seeding and combines linear time recombination with a fast mutation operator to more effectively balance the exploration and exploitation of the search space. The proposed HMOEA computes the optimal Pareto-front solutions in time that is several orders of magnitude lesser than that taken by the exact method, and is shown to obtain superior performance in terms of the convergence and distribution of solutions vis-à-vis the other two extant meta-heuristics on a wide range of benchmark graph instances. Results obtained by the HMOEA are also reported for larger sized Euclidean instances than attempted hitherto in the literature, containing up to 500 vertices and 124,750 edges, and for a benchmark suite of large fully connected random edge-weight graph instances introduced in this work for more effectively comparing the performance of algorithms for the bi-MDCST problem.

Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation

Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two independent parts. However, these two parts are in fact interrelated and often interact with each other. Doing process planning without considering scheduling related factors can easily introduce contradictions or even infeasible solutions. In this work, we propose a mathematical model of integrated process planning and scheduling for remanufacturing (IPPSR), which simultaneously considers the process planning and scheduling problems. An effective hybrid multi-objective evolutionary algorithm (HMEA) is presented to solve the proposed IPPSR. For the HMEA, a multidimensional encoding operator is designed to get a high-quality initial population. A multidimensional crossover operator and a multidimensional mutation operator are also proposed to improve the convergence speed of the algorithm and fully exploit the solution space. Finally, a specific legalising method is used to ‘legalise’ possible infeasible solutions generated by the initialisation method and mutation operator. Extensive computational experiments carried out to compare the HMEA with some well-known algorithms confirm that the proposed HMEA is able to obtain more and better Pareto solutions for IPPSR.

Hybrid multiobjective evolutionary algorithm based on differential evolution for flow shop scheduling problems

This paper proposes a hybrid multiobjective evolutionary algorithm based on differential evolution (HMOEA/DE) to solve the flow shop scheduling problems (FSPs) with the criteria of minimizing the makespan and tardiness simultaneously. Firstly, the hybrid multiobjective evolutionary algorithm (HMOEA) in HMOEA/DE is designed as the global search strategy, which rapidly improves the convergence and distribution performances towards the center and edge regions of Pareto frontier. Secondly, differential evolution (DE) strategy is combined with HMOEA as the local search mechanism to further enhance the convergence and distribution performances on the elite population obtained by HMOEA. Two DE mutation operators are designed for the individuals in the elite population: one is to further improve the performance of each individual and the other serves to much enhance the individual randomly. Numerical comparisons indicate that the efficacy of HMOEA/DE outperforms the traditional multiobjective evolutionary algorithm without DE in convergence and distribution performances on benchmark test problems and FSPs while verifying the advantages and disadvantages of different DE methods.

A Hybrid Multi-Objective Evolutionary Algorithm With Heuristic Adjustment Strategies and Variable Neighbor-Hood Search for Flexible Job-Shop Scheduling Problem Considering Flexible Rest Time

With the development of society, people have a higher demand for the work environment. It has aroused extensive attention of enterprises. One of the most important demands for workers is to have enough rest time to regain strength and energy during the working day. In this paper, a comprehensive mathematical model is established for the multi-objective flexible job-shop scheduling problem with flexible rest time (MOFJSP-FRT). Then a hybrid multi-objective evolutionary algorithm (MOEA) with heuristic adjustment strategies and variable neighborhood search (VNS), named HMOEAV, is proposed to solve the MOFJSP-FRT with the objectives to minimize the makespan and the machines loads balancing simultaneously. In the proposed hybrid algorithm, the machine-based encoding scheme is designed to improve search effectiveness by reducing computational complexity. Two heuristic adjustment strategies considering both the problem characteristics and the objective features are employed to initialize a high-quality population. To adequately emphasize the local exploitation ability of MOEA, VNS is incorporated into it. The non-dominated solutions got by MOEA are the initial solutions for VNS, in which three types of neighborhood structures according to problem structures are designed. The practical case in a steel structure enterprise is carried out to demonstrate the effectiveness of the proposed model and hybrid algorithm. The influences of rest time length on the MOFJSP-FRT are analyzed to give enterprises new insights to improve the scheduling efficiency while ensuring that employees have enough time to rest.

A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm

Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.

Model-based gear-shift optimization for an automated manual transmission

Gear shift quality is traditionally optimized in a vehicle on the road, through the evaluation of the driver’s perception. This classical human-feedback-based method faces different challenges, such as low reproducibility and high cost. In order to overcome these drawbacks, an innovative automatic virtual calibration framework is proposed in the Model-in-the-Loop environment. The devised framework is composed of a dynamic model of the transmission system, an adaptive gear shift controller, an objective assessment of the gear shift quality, and a multi-objective optimization algorithm. The Pareto-optimal front with multi-conflicting criteria, such as shift time and shift comfort, can be obtained within this framework. In particular, an improved hybrid multi-objective evolutionary algorithm is developed to enhance the optimization performance. The optimization of the shift trajectory for an automated manual transmission synchronization system is implemented as a case study. The obtained optimal shift trajectories are also validated on a transmission test bench.

고효율 저유체유발진동 단일채널펌프 설계를 위한 다중목적 최적화

This paper presents the multi-objective optimization of a single-channel pump for wastewater treatment to simultaneously improve the hydraulic efficiency and reduce the unsteady radial force source caused by the impeller-volute interaction. The optimization was carried out by using a hybrid multi-objective evolutionary algorithm coupled with response surface approximation. For analyzing the internal flow in the single-channel pump, steady and unsteady Reynolds-averaged Navier-Stokes equations were solved with the shear stress transport turbulence model as a turbulence closure model. Five design variables related to the internal flow cross-sectional area of the impeller and volute were selected to optimize concurrently the two objective functions with the hydraulic efficiency and the resultant force of the unsteady radial force caused by the impeller-volute interaction. The results of the multi-objective optimization showed the representative arbitrary optimum design considerably enhanced the efficiency and reduced the resultant force of the unsteady radial force caused by the impeller-volute interaction simultaneously, compared to the reference design.

A hybrid multi-objective evolutionary algorithm with feedback mechanism

Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper.

Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

In Wireless Sensor Networks (WSN), maintaining a high coverage and extending the network lifetime are two conflicting crucial issues considered by real world service providers. In this paper, we consider the coverage optimization problem in WSN with three objectives to strike the balance between network lifetime and coverage. These include minimizing the energy consumption, maximizing the coverage rate and maximizing the equilibrium of energy consumption. Two improved hybrid multi-objective evolutionary algorithms, namely Hybrid-MOEA/D-I and Hybrid-MOEA/D-II, have been proposed. Based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D), Hybrid-MOEA/D-I hybrids a genetic algorithm and a differential evolutionary algorithm to effectively optimize sub-problems of the multi-objective optimization problem in WSN. By integrating a discrete particle swarm algorithm, we further enhance solutions generated by Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm. Simulation results show that the proposed Hybrid-MOEA/D-I and Hybrid-MOEA/D-II algorithms have a significant better performance compared with existing algorithms in the literature in terms of all the objectives concerned.

A New Multi-objective Evolutionary Algorithm for Inter-Cloud Service Composition

Service composition in the Inter-Cloud raises new challenges that are caused by the different Quality of Service (QoS) requirements of the users, which are served by different geo-distributed Cloud providers. This paper aims to explore how to select and compose such services while considering how to reach high efficiency on cost and response time, low network latency, and high reliability across multiple Cloud providers. A new hybrid multi-objective evolutionary algorithm to perform the above task called LS-NSGA-II-DE is proposed, in which the differential evolution (DE) algorithm uses the adaptive mutation operator and crossover operator to replace the those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to get the better convergence and diversity. At the same time, a Local Search (LS) method is performed for the Non-dominated solution set F{1} in each generation to improve the distribution of the F{1}. The simulation results show that our proposed algorithm performs well in terms of the solution distribution and convergence, and in addition, the optimality ability and scalability are better compared with those of the other algorithms.

Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

An Efficient Meta-Heuristic for Multi-Objective Flexible Job Shop Inverse Scheduling Problem

In reality, uncertainties may still encounter after a scheduling scheme is generated. These may make the original schedule non-optimal or even impossible. Traditional scheduling methods are not effective in dealing with these situations. In response to this phenomenon, by introducing the idea of inverse optimization into the scheduling field, a new scheduling strategy called “inverse scheduling” has been proposed. To the best of our knowledge, this is the first study to be conducted on flexible job shop inverse scheduling problem (FJISP). In this paper, first, a comprehensive mathematical model with adjustable processing time is established. Then, a hybrid multi-objective evolutionary algorithm based on decomposition and particle swarm optimization is adopted for solving FJISP. To make the proposed algorithm solving FJISP more efficiently, some new strategies are used. A 3-D coding scheme is employed to represent the particles, multiple strategies are designed for generating a high-quality initial population, and effective discrete crossover and mutation operators are specially designed according to the FJISP’s characteristics. Finally, computational experiments are carried out using extended benchmarks, and the results demonstrate the effectiveness of the proposed algorithm for solving the FJISP.

An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem

This paper addresses the multi-objective model for a flexible job shop scheduling problem (FJSSP) to improve the system performance under the condition of machines break down asa real time event. It is important to identify the relevant performance measures to the mentioned problem for examining the system performance. Therefore, minimization of make span and minimization of total machine load variation is considered as two performance measures. Generally, it is very difficult to develop a mathematical model for the real-time situations in FJSSP. Hence, in this paper we divided the research work into two folds: Primarily, a mixed-integer non-linear programming (MINLP) model has been developed to represent the above-mentioned multi-objectives that subjected to constraints without considering machines break down. Secondarily, by incorporating the machines break down as the real-time event the performance of the system is examined. Solving conflicting objectives simultaneously for finding the optimal/near optimal solutions in a reasonable time is a challenge. In this paper, we proposed a new evolutionary based multi-objective teacher learning-based optimization algorithm (MOTLBO) to solve the above-mentioned complex problem. Moreover, to improve the obtained solutions a local search technique has been incorporated in the MOTLBO and comparisons has been made with existing multi-objective particle swarm optimization (MOPSO) and conventional non-dominated sorting genetic algorithm (CNSGA-II). Results found that the proposed multi-objective-based hybrid meta-heuristic algorithm produced high-quality solutions as proved by the tests we performed over a number of randomly generated test problems. Finally, comparisons also made with how the machines break down can affect the proposed systems performance.