Hybrid Genetic Algorithm-simulated Annealing Research Articles

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Proactive dynamic vehicle routing problems considering cooperation services for the store-depot-integrated retailer.

Distribution costs remain consistently high in crowded city road networks, posing challenges for traditional distribution methods in efficiently handling dynamic online customer orders. To address this issue, this paper introduces the Proactive Dynamic Vehicle Routing Problem considering Cooperation Service (PDVRPCS) model. Based on proactive prediction and order-matching strategies, the model aims to develop a cost-effective and responsive distribution system. A novel solution framework is proposed, incorporating a proactive prediction method, a matching algorithm and a hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm. To validate the effectiveness of the proposed model and algorithm, a case study is conducted. The experimental results demonstrate that the dynamic scheme can significantly reduce the number of vehicles required for distribution, leading to cost reduction and increased efficiency.

Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

Hybrid Heuristics for Marker Planning in the Apparel Industry

Given the diversity of styles and sizes in apparel, marker planning which aims to arrange and move all the pattern parts of garments onto a long thin paper before the cutting process is a very important process for the apparel industry. In order to decrease the wastage of fabric after the cutting process, the marker layout essentially needs to be as compact as possible. In this paper, hybrid heuristics are proposed to conduct and achieve an optimized marker layout and length. First, a moving heuristic is presented as a new packing method to arrange and move the patterns without overlapping; here, an initial marker is presented to calculate the length. This heuristic considers multiple rotated angles and flipping positions of the patterns in order to obtain more diverse arrangements. With different arrangements, there is a higher chance of achieving an optimized marker layout and length. To further improve the solution received from the moving heuristic, soft computing algorithms are taken into account, including the genetic algorithm (GA), simulated annealing (SA), and hybrid genetic algorithm-simulated annealing (HGASA) to achieve a shorter length in the marker layout. The best marker length can be obtained from HGASA, which can save almost 28% of the length. Special cases with specific combinations in rotated angles are considered so that the industry can make informed choices on the algorithms suitable to address the marker planning problem.

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.

A Comparative Study of Meta-Heuristic Optimization Algorithms for 0 – 1 Knapsack Problem: Some Initial Results

In this paper, we present some initial results of several meta-heuristic optimization algorithms, namely, genetic algorithms, simulated annealing, branch and bound, dynamic programming, greedy search algorithm, and a hybrid genetic algorithm-simulated annealing for solving the 0-1 knapsack problems. Each algorithm is designed in such a way that it penalizes infeasible solutions and optimizes the feasible solution. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The numerical results of the hybrid algorithm are compared with the results achieved by the individual algorithms. The results revealed the superior performances of the branch and bound dynamic programming, and hybrid genetic algorithm with simulated annealing methods over all the compared algorithms. This performance was established by taking into account both the algorithm computational time and the solution quality. In addition, the obtained results also indicated that the hybrid algorithm can be applied as an alternative to solve small- and large-sized 0-1 knapsack problems.

A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management

In this paper, we present an integrated production-distribution (P-D) model which considers rail transportation to move deteriorating items. The problem is formulated as a mixed integer programming (MIP) model, which could then be solved using GAMS optimization software. A hybrid genetic algorithm-simulated annealing (GA-SA) is developed to solve the real-size problems in a reasonable time period. The solutions obtained by GAMS are compared with those obtained from the hybrid GA-SA and the results show that the hybrid GA-SA is efficient in terms of computational time and the quality of the solution obtained.

Forecasting of Turkey natural gas demand using a hybrid algorithm

ABSTRACTThe basis of the energy management constitutes the forecasting of the need for energy as much as accurate. In this study, a hybrid genetic algorithm-simulated annealing (GA-SA) algorithm based on linear regression has been developed and coded as software to forecast natural gas demand of Turkey. The linear models, which are constructed by using the amounts of natural gas consumption for years between 1985 and 2000 as dependent variable, and gross national product, population, and growth rate as independent variables, are used to forecast the amount of natural gas consumption for years between 2001 and 2009. Then, the forecasts are compared with real amounts of consumptions and are analyzed statistically.Consequentially, it is observed that the GA-SA hybrid algorithm made forecasts with less statistical error against linear regression. The models were used to forecast Turkey’s natural gas demand under two different scenarios for years between 2010 and 2030.

Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm

We consider a joint decision model of worker assignment and production planning in a dynamic cellular manufacturing system of fiber connector manufacturing industry. On one hand, due to the learning and forgetting effects of workers, the production rate of each workstation will often change. Thus, the bottleneck workstation may transfer to another one in the next period. It is worthwhile to reassign multi-skilled workers such that the production rate of bottleneck workstation may increase. On the other hand, because of the limited production capacity and variety of orders, late delivery or production in advance often occurs at each period. The parts with operational sequence need to be dispatched to the desirable cells for processing. The objective is to minimize backorder cost and holding cost of inventory. To solve this complicated problem, we propose an efficient hybrid bacteria foraging algorithm (HBFA) with elaborately designed solution representation and bacteria evolution operators. A two-phase based heuristic is embedded in the HBFA to generate a high quality initial solution for further search. We tested our algorithm using randomly generated instances by comparing with the original bacteria foraging algorithm (OBFA), discrete bacteria foraging algorithm (DBFA), hybrid genetic algorithm (HGA) and hybrid simulated annealing (HSA). Our results show that the proposed HBFA has better performance than the four compared algorithms with the same running time.

A Hybrid Algorithm for Minimizing Makespan in the Permutation Flow Shop Scheduling Environment

Hybrid Genetic Algorithm-Simulated Annealing algorithm (HGASA) is proposed for explaining the permutation flow shop scheduling issue (PFSP) with minimizing makespan foundation. We have displayed a hybrid algorithm (HGASA) consolidates the great component of both the Genetic Algorithm and the simulated annealing algorithm. The HGASA is tried with outsized flow shop scheduling bench mark problems from OR Library and the test results are compared with earlier reported results of particle swarm optimization (PSO) algorithm, and a well-known bacterial foraging optimization algorithm (BFO). Almost, 45 famous benchmark problems were utilized to check the execution of proposed HGASA. The test comes about demonstrate that HGASA performs well with the other algorithm for all cases from the literature. The complexity of the proposed HGASA is found to be better than that of PSO and BFO. The obtained results demonstrates the viability of proposed HGASA.

A novel bi-level hierarchy towards available-to-promise in mixed-model assembly line sequencing problems

This article addresses advanced available-to-promise (AATP) in mixed-model assembly line sequencing problems. In the developed framework, customers are prioritized with respect to 11 defined criteria using the technique for order of preference by similarity to ideal solution (TOPSIS) method, and order quantities are calculated using a nonlinear mathematical program. Next, a mixed binary nonlinear mathematical program is developed to determine the optimum sequence of the optimized order quantities to minimize the total lateness. Since the proposed models are intractable, a hybrid genetic algorithm–simulated annealing method is also developed. Finally, an industrial case study is reported, the results of which validate the developed AATP framework.

An optimization technique using hybrid GA-SA algorithm for multi-objective scheduling problem

A Mixed-model assembly line is widely employed to perform the assembly operation in industries and the time needed to release products to market is frequently considered by many researchers. However, providing an appropriate level of flexibility to meet customer demand variations is critical for companies survival in this competitive market. The problem of production planning in terms of sequencing various product model is studied here. A manufacturing system is presented to show the application of this problem. A proposed multi-objective function is given to minimize the overall make-span of a mixed-model assembly line, but with additional goals also considered, such as balancing the assembly line and minimizing the variation of completion time. We propose a solution aimed to solve the problem in successive stages. For each stage, a mathematical model formally describes the problem and the main difficulties faced are explained. Due to the high complexity of problem solving procedures by classical mathematic techniques, this paper presents a new approach of hybrid genetic algorithm-simulated annealing (GA-SA) implementation in order to meet the problem objectives. A proposed hybrid scheme is executed to overcome problem complexity and to meet the problem objectives. In order to check the efficiency of hybrid search techniques, a comparison is made between the results obtained by hybrid GA-SA and GA, and the comparison validates the effectiveness of the presented hybrid search technique.

Hybrid evolutionary algorithms in a SVR traffic flow forecasting model

Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt–Winters (HW) and seasonal Holt–Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow.

DMLHFLC (Dual mode linguistic hedge fuzzy logic controller) for an isolated wind–diesel hybrid power system with BES (battery energy storage) unit

Abstract Design of DMLHFLC (dual mode linguistic hedge fuzzy logic controller) for an isolated wind–diesel hybrid power system with BES (battery energy storage) unit is proposed in this paper. The design methodology is a hybrid model based on the concepts of linguistic hedges and hybrid genetic algorithm-simulated annealing algorithms. Dual-mode concept is also incorporated in this proposed controller because it can improve the system performance. The system is simulated and the results are compared with proportional plus integral controller and FLC (fuzzy logic controller). Sensitivity analysis and robustness of the controller is tested. The results prove the effectiveness of the proposed controller.

Design of Dual Mode Linguistic Hedge Fuzzy Logic Controller for an Isolated Wind-Diesel Hybrid Power System with Lossy Magnetic Energy Storage Unit

In this paper, a dual mode linguistic hedge fuzzy logic controller for an isolated wind-diesel hybrid power system with lossy magnetic energy storage unit is proposed. The design methodology is a hybrid model based on the concepts of dual mode control, the linguistic hedges and the hybrid genetic algorithm-simulated annealing algorithms. The system is simulated with the dual mode linguistic hedge fuzzy logic controller and the frequency deviation resulting from the step load disturbance is presented. It is observed from the result that with the application of the proposed controller, the system performance is improved significantly. It has been also found that the proposed controller is less sensitive to the changes in the parameters of the system and also under different operating modes of the hybrid power system.

Dual mode linguistic hedge fuzzy logic controller for an isolated wind–diesel hybrid power system with superconducting magnetic energy storage unit

A design of dual mode linguistic hedge fuzzy logic controller for an isolated wind–diesel hybrid power system with superconducting magnetic energy storage unit is proposed in this paper. The design methodology of dual mode linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of linguistic hedges and hybrid genetic algorithm-simulated annealing algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically and can speed up the control result to fit the system demand. The hybrid genetic algorithm–simulated annealing algorithm is adopted to search the optimal linguistic hedge combination in the linguistic hedge module. Dual mode concept is also incorporated in the proposed controller because it can improve the system performance. The system with the proposed controller was simulated and the frequency deviation resulting from a step load disturbance is presented. The comparison of the proportional plus integral controller, fuzzy logic controller and the proposed dual mode linguistic hedge fuzzy logic controller shows that, with the application of the proposed controller, the system performance is improved significantly. The proposed controller is also found to be less sensitive to the changes in the parameters of the system and also robust under different operating modes of the hybrid power system.

Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation

AbstractWe present a novel approach for optimizing reservoir operation through fuzzy programming and a hybrid evolution algorithm, i.e. genetic algorithm (GA) with simulated annealing (SA). In the analysis, objectives and constraints of reservoir operation are transformed by fuzzy programming for searching the optimal degree of satisfaction. In the hybrid search procedure, the GA provides a global search and the SA algorithm provides local search. This approach was investigated to search the optimizing operation scheme of Shihmen Reservoir in Taiwan. Monthly inflow data for three years reflecting different hydrological conditions and a consecutive 10‐year period were used. Comparisons were made with the existing M‐5 reservoir operation rules. The results demonstrate that: (1) fuzzy programming could effectively formulate the reservoir operation scheme into degree of satisfaction α among the users and constraints; (2) the hybrid GA‐SA performed much better than the current M‐5 operating rules. Analysis also found the hybrid GA‐SA conducts parallel analyses that increase the probability of finding an optimal solution while reducing computation time for reservoir operation. Copyright © 2007 John Wiley & Sons, Ltd.

Discussion of “Optimal In Situ Bioremediation Design by Hybrid Genetic Algorithm-Simulated Annealing” by Horng-Jer Shieh and Richard C. Peralta

Discussion of “Optimal In Situ Bioremediation Design by Hybrid Genetic Algorithm-Simulated Annealing” by Horng-Jer Shieh and Richard C. Peralta

Closure to “Optimal In Situ Bioremediation Design by Hybrid Genetic Algorithm-Simulated Annealing” by Horng-Jer Shieh and Richard C. Peralta

Closure to “Optimal In Situ Bioremediation Design by Hybrid Genetic Algorithm-Simulated Annealing” by Horng-Jer Shieh and Richard C. Peralta

Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control

In this paper, various novel heuristic stochastic search techniques have been proposed for optimization of proportional–integral–derivative gains used in Sugeno fuzzy logic based automatic generation control of multi-area thermal generating plants. The techniques are classical particle swarm optimization, hybrid particle swarm optimizations and hybrid genetic algorithm simulated annealing. Numerical results show that all optimization techniques are more or less equally very effective in yielding optimal transient responses of area frequency and tie-line power flow deviations. The gains obtained by particle swarm optimization are more optimal than those obtained by GA/hybrid GA-simulated annealing. Particle swarm optimizations take the least time to achieve the same optimal gains. These gains are for nominal system parameters. For varying off-nominal on-line system parameters, fast acting Sugeno fuzzy logic manipulates the nominal gains adaptively to determine transient responses.