Hybrid Multi-objective Genetic Algorithm Research Articles

Simultaneous efficiency improvement of pump and turbine modes for a counter-rotating type pump-turbine

This article presents a multi-objective optimization to improve the hydrodynamic performance of a counter-rotating type pump-turbine operated in pump and turbine modes. The hub and tip blade angles of impellers/runners with four blades, which were extracted through a sensitivity test, were optimized using a hybrid multi-objective genetic algorithm with a surrogate model based on Latin hypercube sampling. Three-dimensional steady incompressible Reynolds-averaged Navier–Stokes equations with the shear stress transport turbulence model were discretized via finite volume approximations and solved on a hexahedral grid to analyze the flow in the pump-turbine domain. For the major hydrodynamic performance parameters, the pump and turbine efficiencies were selected as the objective functions. Global Pareto-optimal solutions were searched using the response surface approximation surrogate model with the non-dominated sorting genetic algorithm, which is a multi-objective genetic algorithm. The trade-off between the two objective functions was determined and described with regard to the Pareto-optimal solutions. As a result, the pump and turbine efficiencies for the arbitrarily selected optimum designs in the Pareto-optimal solutions were increased as compared with the reference design.

Solving a Bi-Objective Transportation Vehicle Routing Problem for Waste Collection by a New Hybrid Algorithm

This paper is an extension of the well-known vehicle routing problem (VRP) consisting of two stages. The first and second stages deal with the vehicle routing and transportation problems, respectively. Waste collection is one of the applications of the considered problem in a real world situation. A new mathematical model for this type of the problem is presented that minimizes the waste collection cost and decreases the risk posed to the environment for hazardous wastes transportation simultaneously. According to the NP-hard nature of the problem, a new multi-objective hybrid cultural and genetic algorithm (MOHCG) is proposed to obtain Pareto solutions. A straightforward representation for coding the given model is proposed to help us in reducing the computational time. To validate the proposed algorithm, a number of test problems are conducted and the obtained results are compared with the results of the well-known multi-objective evolutionary algorithm, namely non-dominated sorting genetic algorithm (NSGA-II), with respect to some comparison metrics. Finally, the conclusion is provided.

A multi-objective hybrid GA and TOPSIS approach for sizing and siting of DG and RTU in smart distribution grids

Installing remote terminal units (RTUs) in smart distribution grids enables the distribution system operator (DSO) and end-side customers to properly execute demand response programs (DRPs). Direct load control (DLC) refers to an incentive-based DRP, in which, DSO contracts with customers to control a specific percentage of their consumptions. In initial stages, the limited budget and technical hurdles avert the widespread deployment of RTUs in all buses. This issue instigates the need for an optimal placement of RTUs. Moreover, the existence of RTUs definitely affects the load shape of the network. Thus, the optimal placement strategy for distributed generations (DGs) should be revisited. To involve these issues together, an optimal two-stage multi-objective procedure is proposed which takes into account the simultaneous placement of DGs and RTUs. Different scenarios including variations in the number of DG units, DGs’ adaptive power factor (APF) for reactive power processes, and individual or simultaneous placement of DGs and RTUs are established and interrogated in depth. In numerical validations, both of the power losses minimization and voltage profile improvement are explored based on an AC power flow fashion optimized with genetic algorithm (GA). The extracted results are then transferred to the second stage which applies a multi-attribute decision making approach based on technique for order preference by similarity to ideal solution (TOPSIS). This stage considers the importance degrees or so-called weights for both of the objectives. In this way, the worth of each objective is suitably determined and involved in decision making process. Thus, the optimal placement strategy for simultaneous allocation of DGs and RTUs is determined.

Reducing the wasted transportation capacity of Personal Rapid Transit systems: An integrated model and multi-objective optimization approach

This study addresses the problem of wasted transportation capacity in Personal Rapid Transit (PRT) systems. We propose a two-tier transportation model that integrates PRT and capillary transportation systems. We study a related multi-objective empty vehicle redistribution problem that attempts to minimize empty movement and the number of vehicles used. Furthermore, we design a hybrid multi-objective genetic algorithm that integrates multiple crossover operators and linear programming techniques to solve the proposed problem. Evaluations indicate that our algorithm produces satisfactory results, and simulations confirm the efficiency of our proposed two-tier transportation system.

Intelligent multi-objective control and management for smart energy efficient buildings

Abstract Energy management in buildings has become an increasing trend in their transformation to smart and efficient in utilizing energy resources. The potential affinity of these buildings is coping energy sustainability, security and reliability. Building energy management has been primarily associated with development and implementation of an efficient control scheme. The challenging task of building controls is to achieve indoor building environment comfort with improved energy efficiency. In this study, multi-agent control system has been developed in combination with stochastic intelligent optimization. The multi-objective genetic algorithm (MOGA) and hybrid multi-objective genetic algorithm (HMOGA) are used as optimization algorithms. The corresponding case study simulations of effective management of energy and user comfort are presented. The developed control system provides substantial enhancement in energy efficiency and indoor environmental comfort in smart buildings. An energy efficiency of 31.6% has been achieved with an 8.1% improvement of comfort index using the HMOGA technique.

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.

Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment

The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements.

Multi-objective optimization of solar air heater with obstacles on absorber plate

A multi-objective optimization of a solar air heater with obstacles on an absorber plate is performed for maximum heat transfer and minimum pressure loss. In this work, shape optimization is carried out in conjunction with three-dimensional Reynolds-averaged Navier–Stokes analysis and two basic surrogate models: the response surface approximation and the Kriging models. Three geometric variables (the ratio of the obstacle height to the height of the duct; the ratio of the transverse pitch to the base length of the obstacle; and the angle of attack) were used as design variables for the optimization. The average Nusselt number and friction factor were used to define the two objective functions. The Latin hypercube sampling method was used to select the design points in the design space. A hybrid multi-objective genetic algorithm coupled with the surrogate model was used to find the Pareto-optimal solutions. The representative Pareto-optimal solutions were selected to study the trade-off between the two objectives. The response surface approximation model leads to a better set of non-dominated solutions over a wide range of functional space than the Kriging model. The optimization results show that the objective functions are significantly affected by the design variables, and the constructed surrogate models show good prediction accuracies for the objective functions. A performance factor was used to study the thermal–hydraulic performance of the Pareto-optimal solutions.

Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization

AbstractModal strain energy (MSE) is a sensitive physical property that can be utilized as a damage index in structural health monitoring. Inverse problem solving‐based approaches using single‐objective optimization algorithms are also a promising damage identification method. However, the research into the integration of these methods is currently limited; only partial success in the detection of structural damage with high errors has been reported. The majority of previous research was focused on detecting damage in simply supported beams or plain structures. In this study, a novel damage detection approach using hybrid multiobjective optimization algorithms based on MSE is proposed to detect damages in various three‐dimensional (3‐D) steel structures. Minor damages have little effect on the difference of the modal properties of the structure, and thus such damages with multiple locations in a structure are difficult to detect using traditional damage detection methods based on modal properties. Various minor damage scenarios are created for the 3‐D structures to investigate the newly proposed multiobjective approach. The proposed hybrid multiobjective genetic algorithm detects the exact locations and extents of the induced minor damages in the structure. Even though it uses incomplete mode shapes, which do not have any measured information at the damaged element, the proposed approach detects damage well. The robustness of the proposed method is investigated by adding 5% Gaussian random white noise as a noise effect to mode shapes, which are used in the calculation ofMSE.

Best Compromise Alternative to EELD Problem using Hybrid Multiobjective Quantum Genetic Algorithm

A novel hybrid multiobjective quantum genetic algorithm (HM-QGA) for economic emission load dispatch (EELD) optimization problem is presented. The EELD problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. HM-QGA are population based evolutionary algorithms that imitate quantum physics by introducing quantum bits for a basic probabilistic genotypic representation a nd hence better population diversity, and quantum gates for evolving the population of solutions. We use quantum genetic algorithm to exploits the power of quantum computing to speed up genetic algorithm procedure. We present methodology that allows the decision maker (DM) to be a partner in problem solving, where DM specifies input values (namely the weight values) according his need s. Simulation results on the standard IEEE 30-bus 6-generator test system show that the proposed algorithm outperforms other heuristic algorithms and is characterized by robustness, high success, fast convergence and excellent capability on global searching.

A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2)

This paper presents a new extension of SALBP-2, so called assembly line resource assignment and balancing problem of type 2 (ALRABP-2). Two main differences from the existing literature are revealed in this work. The first is on the objective function which is a multiple one. It is aimed here to minimize both the cycle time and the cost per time unit (hour) of a line for a fixed number of stations to satisfy the constraints of precedence between tasks and compatibility between resources. The second difference lies in the proposed method to solve this problem. A new version of multi-objective genetic algorithm (MOGA) called hybrid MOGA (HMOGA) is elaborated. Full experiment design is used to obtain a better MOGA parameters combination. The effectiveness of the HMOGA was assessed through a set of literature problems. The performance of HMOGA shows a good quality of the fronts generated and a better problem-solving capacity for two optimisations.

A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem

In this paper, a multi-objective flexible job shop scheduling problem with machines capacity constraints is studied. Minimizing the makespan and overtime costs of machines are considered as two objectives for evaluating solutions. First, a new nonlinear integer programming model is presented to formulate the problem. Inasmuch as this problem is well-known as a NP-hard problem, a hybrid meta-heuristic algorithm (CFJSP II) is developed to overcome its complexity. Regarding to the solution space of the problem, for assigning and sequencing operations, a multi-objective genetic algorithm based on the ELECTRE method is presented. Also, a powerful heuristic approach to tradeoff the objective functions is developed. Finally, the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2, and VEGA. Regarding to the computational results, it is clear that the proposed algorithm has a better performance especially in the closeness of the solutions to the Pareto optimal front.

A Multiobjective Hybrid Genetic Algorithm for TFT-LCD Module Assembly Scheduling

The thin-film transistor-liquid crystal display (TFT-LCD) module assembly production is a flexible job-shop scheduling problem that is critical to satisfy the customer demands on time. On the module assembly shop floor, each workstation has identical and non-identical parallel machines that access the jobs at various processing velocities depending on the product families. To satisfy the various jobs, the machines need to be set up as the numerous tools to conduct consecutive products. This study aims to propose a novel approach to address the TFT-LCD module assembly scheduling problem by simultaneously considering the following multiple and often conflicting objectives such as the makespan, the weighted number of tardy jobs, and the total machine setup time, subject to the constraints of product families, non-identical parallel machines, and sequence-dependent setup times. In particular, we developed a multiobjective hybrid genetic algorithm (MO-HGA) that hybridizes with the variable neighborhood descent (VND) algorithm as a local search and TOPSIS evaluation technique to derive the best compromised solution. To estimate the validity of the proposed MO-HGA, experiments based on empirical data were conducted to compare the results with conventional approaches. The results have shown the validity of this approach. This study concludes with a discussion of future research directions.

Multi-objective highway alignment optimization incorporating preference information

This paper presents a GIS-based multi-objective optimization model, particularly designed to aid highway engineers and planners in proposing competitive highway alignment alternatives when building a new highway or expanding an existing highway. The proposed model can effectively examine tradeoffs among various objectives that represent possibly conflicting interests of different stakeholders. A Hybrid Multi-Objective Genetic Algorithm, which utilizes designers’ knowledge about the preference of decision makers, is developed to search for a set of Pareto-optimal solutions with an acceptable level of diversity. Two case studies demonstrate the capability of the proposed approach in providing multiple trade-off solutions. The results indicate that the incorporation of preference information, even if preliminary in nature, has great potential to save computation time and improve the quality of the obtained Pareto-optimal set.

A multi-objective hybrid genetic algorithm to minimize the total cost and delivery tardiness in a reverse logistics

In the recent environmental protection the reverse logistics of the used product is one of the most important research topics. The reverse logistics is the process flow of used-products that are collected to be reproduced so that they can be sold again to customers after some processing. We propose a multi-objective hybrid genetic algorithm (mo-hGA) combined with Fuzzy Logic Controller (FLC) for efficiently dealing with multi-objective reverse logistics network (mo-RLN) problem. The aim of this paper is firstly to formulate mo-RLN model, and secondly to optimize it by mo-hGA method proposed with reusable system configuration. In particular two objective functions to be minimized total costs of mo-RLN, (i.e. fixed opening cost, transportation cost and inventory cost) and also minimized delivery tardiness in all periods are considered in the model. We will clear each objective function (i.e. total costs and total delivery tardiness), computational time and number of Pareto solutions with LINGO, pri-awGA (priority-based GA with adaptive weight approach) and mo-hGA proposed with numerical examples. For demonstrating the effectiveness of the proposed model, we evaluate with the numerical examples and simulate it with a bottles distilling/sale company as a case study in Busan, Korea.

A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines

Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem.

Development of a Multi-objective Genetic Algorithm for MDO Problem

Engineering systems have become quite complicated in recent days. The requirements of design are complex and it is hard to meet them by considering only one discipline. In this paper, we suggest a hybrid Multi-objective Genetic Algorithm (MOGA) for a Multidisciplinary Design Optimization (MDO) model in engineering design. In order to get Pareto optimal solutions, we use local search to enhance the efficiency and effectiveness. We solve a problem of airplane wings optimization using MOGA. After compared with other MDO algorithms, we find the result shows that the proposed algorithm’s performance has an advantage over previous work for these MDO problems. As the algorithm is evaluated based on practical data, we show that the approach can be used to solve MDO problems in the real world.

309 セル生産のための多目的ハイブリッド遺伝的アルゴリズムを用いた動的生産管理システムの開発とその評価(OS3-2 生産スケジューリング・生産管理II)

309 セル生産のための多目的ハイブリッド遺伝的アルゴリズムを用いた動的生産管理システムの開発とその評価(OS3-2 生産スケジューリング・生産管理II)

A Hybrid Multiobjective Genetic Algorithm for Robust Resource‐Constrained Project Scheduling with Stochastic Durations

We study resource‐constrained project scheduling problems with perturbation on activity durations. With the consideration of robustness and stability of a schedule, we model the problem as a multiobjective optimization problem. Three objectives—makespan minimization, robustness maximization, and stability maximization—are simultaneously considered. We propose a hybrid multiobjective evolutionary algorithm (H‐MOEA) to solve this problem. In the process of the H‐MOEA, the heuristic information is extracted periodically from the obtained nondominated solutions, and a local search procedure based on the accumulated information is incorporated. The results obtained from the computational study show that the proposed approach is feasible and effective for the resource‐constrained project scheduling problems with stochastic durations.

A hybrid multi-objective genetic algorithm for planning order release date in two-level assembly system with random lead times

Supply planning of two-level assembly system is considered in this paper in which lead times are stochastic at both levels. Finding optimal order release dates of components is our aim which optimizes problem’s performance metrics. There is a determined due date that if demands are not prepared for delivery in that day, backlogging cost would happen. The other noticeable cost that should be taken in to consideration is holding cost, which may occur if components arrive before their time of assembling. In contrast to most of studies in this field that consider sum of these cost, this paper attempts to make a tradeoff between these objectives’ affects using multi-objective approach. This approach hybridizes an evolutionary optimizer, genetic algorithm, with an electromagnetism-like mechanism (EM). The algorithm’s effectiveness is compared against an adapted non-dominated sorting genetic algorithm II (NSGA-II) which has been presented for this problem.