Efficient Hybrid Genetic Algorithm Research Articles

An efficient hybrid genetic algorithm for the traveling salesman problem with release dates

This paper deals with a generalization of the classic traveling salesman problem where each customer is associated with a release date, defined as the time at which the desired product becomes available at the depot. In this problem, a single uncapacitated vehicle is allowed to perform multiple trips in order to satisfy all demands. However, the vehicle cannot start a route unless all the products associated with the demands in the route are released. As a consequence, there might be a waiting time before starting the next route. The objective of the problem is to minimize the completion time of the service, defined as the time at which the vehicle returns to the depot after satisfying all demands. We propose a hybrid genetic algorithm that incorporates more advanced mechanisms to evaluate individuals and to ensure population diversity. We also introduce a novel dynamic programming splitting algorithm that converts a sequence of visits to customers, the so-called giant-tour, into the best set of routes that respects the sequence. Computational experiments performed on 522 benchmark instances show that our approach is able to find all 154 known optimal solutions from the literature. In addition, we were able to improve the best-known upper bounds for 364 instances in significantly shorter computational times when compared to the state-of-the-art.

A hybrid approach to enhanced genetic algorithm for route optimization problems

Shortest path problem has emerged to be one of the significant areas of research and there are various algorithms involved in it. One of the successful optimization techniques is genetic algorithm (GA). This paper proposes an efficient hybrid genetic algorithm where initially we use a map reduction technique to the graph and then find the shortest path using the conventional genetic algorithm with an improved crossover operator. On comparing this hybrid algorithm with other algorithms, it has been detected that the performance of the modified genetic algorithm is better as comparison to the other methods in terms of various metrics used for the evaluation.

An efficient hybrid genetic algorithm for solving truncated travelling salesman problem

This paper considers a practical truncated traveling salesman problem (TTSP), in which the salesman is only required to cover a subset of out of given cities (rather than covering all the given cities as in conventional travelling salesman problem (TSP)) with minimal traversal distance. Thus, every feasible solution tour contains exactly cities including the starting city. However, extensive research on TSP has been received and various efficient solution techniques including exact, heuristic, and metaheuristic algorithms are devoted, a very limited attention has been given to TTSP models because of its solution structure. The TTSP model comprises two types of problems including city selection i.e. as a salesman's trip need not include all the cities, the challenge is to identify which combination of cities are to be visited and which sequence of cities will constitute minimal traversal distance. A hybrid genetic algorithm (GA) comprising sophisticated mutation operators is developed to tackle this problem efficiently. Comparative computational findings suggest that the proposed GA has capability to outperform existing approaches in terms of TTSP results. In addition, the proposed GA report improved results and will serve as a basis for forthcoming TTSP studies.

Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures, but identification of seizures from EEG remains a challenging task that requires qualified neurophysiologists. It is important to detect seizures in real time, which can be achieved in an internet of things (IoT)-based cloud platform to alert patients of impending seizures. Therefore, in this study, we propose a new model to recognize seizure states (e.g., ictal, preictal, interictal) from EEG in the IoT framework to monitor patients remotely. The proposed model uses an efficient hybrid genetic whale optimization algorithm (GWOA) based on naïve Bayes (NB-GWOA) for feature selection, and an adaptive extreme learning machine (ELM) based on a differential evolutionary (DE) algorithm (DEELM) for classification. In the NB-GWOA method, the genetic algorithm serves to enhance the exploitation of the whale optimization algorithm in the search of the optimal solutions, while the naïve Bayes method is used to determine a fitness function to assess every agent in the search space. GWOA has strong robustness and is capable of finding the best solutions in less than five iterations, so it is suitable for selecting discriminative features from a huge number of neurofeatures obtained from EEG. Further, the classification model is constructed based on ELM, which uses the DE algorithm for a fast and efficient learning solution. Results show that the proposed NB-GWOA-DEELM model can avoid over- and under-fitting and can provide better and more accurate performance in classifying seizure states from EEG than its competitors.

TU34. ASSOCIATION BETWEEN DEPRESSION DIAGNOSIS, POLYGENIC RISK FOR DEPRESSION AND ANTIHYPERTENSIVE MEDICATION NON-PERSISTENCE

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures, but identification of seizures from EEG remains a challenging task that requires qualified neurophysiologists. It is important to detect seizures in real time, which can be achieved in an internet of things (IoT)-based cloud platform to alert patients of impending seizures. Therefore, in this study, we propose a new model to recognize seizure states (e.g., ictal, preictal, interictal) from EEG in the IoT framework to monitor patients remotely. The proposed model uses an efficient hybrid genetic whale optimization algorithm (GWOA) based on naïve Bayes (NB-GWOA) for feature selection, and an adaptive extreme learning machine (ELM) based on a differential evolutionary (DE) algorithm (DEELM) for classification. In the NB-GWOA method, the genetic algorithm serves to enhance the exploitation of the whale optimization algorithm in the search of the optimal solutions, while the naïve Bayes method is used to determine a fitness function to assess every agent in the search space. GWOA has strong robustness and is capable of finding the best solutions in less than five iterations, so it is suitable for selecting discriminative features from a huge number of neurofeatures obtained from EEG. Further, the classification model is constructed based on ELM, which uses the DE algorithm for a fast and efficient learning solution. Results show that the proposed NB-GWOA-DEELM model can avoid over- and under-fitting and can provide better and more accurate performance in classifying seizure states from EEG than its competitors.

Improving construction and demolition waste collection service in an urban area using a simheuristic approach: A case study in Sydney, Australia

Urbanization and population growth have resulted in a significant increase in the amount of generated construction and demolition (C&D) waste worldwide. Improper C&D waste management has led to a tremendous landfilled C&D waste, which has placed a great concern over its adverse impacts on the environment and natural resources. Appropriate C&D waste recycling mechanism as a remedial action saves our resources from deterioration, which can be guaranteed through a systematic apportion of the construction projects to C&D waste recycling facilities. Previous studies in waste collection routing problem have almost exclusively assumed that parameters are deterministic; however, uncertainty associated with waste collection routing makes deterministic models inapplicable to real-life systems. To tackle this problem, this research proposes a novel simheuristic based on an integrated simulation-optimization approach, in which an efficient hybrid Genetic Algorithm (GA) is applied in order to optimize vehicle route planning for C&D waste collection from construction projects to recycling facilities. A comparative analysis with existing well-known approaches is performed to represent the strength and effectiveness of the proposed approach. The results demonstrate high performance of the proposed simheuristic algorithm. This study has also benefited from a real case of construction projects apportion to recycling facilities in Sydney, Australia for better evaluation. This research strongly contributes to academics by lighting up the ways to optimize future waste collection problems in a wider range and more precise manner. Meanwhile, this study recommends to C&D waste decision makers and practitioners to allocate generated C&D waste to recycling facilities precisely with respect to the capacity of produced C&D waste, capacity of recycling facilities, distances, and vehicle capacities.

Wartime logistics model for multi‐support unit location–allocation problem with frontline changes

AbstractTo reflect a realistic, changing front line, wartime logistics are illustrated by a dynamic location–allocation model. In this paper, a mixed integer programming (MIP) model is developed for use in deciding the timing of unit relocation for continuous resupply, safe locations for support units, and delivery amounts that minimize total risk to the logistics service. Total risk in wartime logistics is represented by unsatisfied demand, hazard at the support site, and the number of relocations. The proposed MIP model reflects realistic factors in battle situations, such as maximum distance, vehicle capacity, basic load carried by combat units, and limited supplies during unit relocation. Furthermore, special operators for crossover and mutation are developed to maintain feasibility of possible solutions, and an efficient hybrid genetic algorithm is proposed to find optimal and near‐optimal solutions.

A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems

An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO)is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation.

Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

An efficient hybrid genetic algorithm for multi-product competitive supply chain network design with price-dependent demand

According to the recent studies, competition between supply chains (SCs) will play a noticeable part of competition in the future’s market. This study considers a SC vs. SC multi-product competition in the duopolistic markets. Pricing, location, transportation and production decisions are taken in a Stackelbrg game formulated as bi-level model. These supply chains (called leader and follower) are three-stage, multi-product, multi-source, and single-period SCs. Karush-Kuhn-Tucker (KKT) conditions are applied in order to transform the presented bi-level model into a single-level mixed integer nonlinear programming (MINLP). Due to computational complexity of the problem, an efficient Hybrid Genetic Algorithm (HGA) is proposed to solve the large size problems. In order to obtain more robust solutions, Taguchi method is applied to calibrate hybrid meta-heuristic parameters. The efficiency of the proposed HGA is investigated through the comparisons with optimal solutions and also with GA. Managerial insights are proposed alongside with the Sensitivity Analysis for important parameters.

Design of a Hybrid Genetic Algorithm for Parallel Machines Scheduling to Minimize Job Tardiness and Machine Deteriorating Costs with Deteriorating Jobs in a Batched Delivery System

This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizing total tardiness, delivery, holding and machine deteriorating costs. The problem of total tardiness on identical parallel machines is NP-hard, thus the under investigation problem, which is more complicated, is NP-hard too. In this study, a mixed-integer programming (MILP) model is presented and an efficient hybrid genetic algorithm (HGA) is proposed to solve the concerned problem. A new crossover and mutation operator and a heuristic algorithm have also been proposed depending on the type of problem. In order to evaluate the performance of the proposed model and solution procedure, a set of small to large test problems are generated and results are discussed. The related results show the effectiveness of the proposed model and GA for test problems.

Inventory routing problem for hazardous and deteriorating items in the presence of accident risk with transshipment option

In this study, we address an Inventory Routing Problem for hazardous and deteriorating pharmaceutical items in a healthcare network. Each hospital's demand is assumed to be deterministic but time varying over a finite planning horizon. Demand can be satisfied through a supply network, either from the main central pharmacy, or from other related hospitals' drugstores by allowing the transshipment option. The medicines deteriorate under a constant rate during the storage period. The proposed model is a bi-objective mixed integer mathematical programming. The first objective aims to minimize the total cost of logistics including ordering, transportation, delivery, pickup, shortage and inventory holding costs. The second objective function attempts to minimize the maximum accident loss during distribution among all periods. To consider the accident loss, for each route, two essential parameters are estimated based on historical data; the occurrence probability and severity index of accident. The model therefore attempts to utilize the best configuration of the routes and transshipment option to satisfy the demand while minimizing the costs and accident loss simultaneously. Several numerical examples are generated and solved by CPLEX and compared with the solutions of an efficient Hybrid Genetic Algorithm (HGA). The results show that the transshipment option not only can be used as a lever to increase the economic supply network performance through saving the routes, but also it can help the system to avoid risky routes.

An Improved Genetic Algorithm Approach on Mechanism Kinematic Structure Enumeration with Intelligent Manufacturing

During the process of mechanism kinematic structure enumeration, isomorphism identification of graphs is an important and complicated problem. The problem is known to be a NP-complete problem. In this paper, according to the mechanism kinematic chain isomorphism identification criteria, a highly efficient hybrid genetic algorithm model is proposed for isomorphism identification. The model method is coupled with genetic algorithm, optimal choice, and optimal crossover operation. It shows a quick convergence rate of the late operation and can avoid convergence to local optimum. Simulation results show that the hybrid algorithm is more rapid and effective compared with simple genetic algorithm and the improved neural network algorithm.

Two-Phase Approach for Designing Bus-Based Park-and-Ride System: Case Study of Isfahan, Iran

AbstractPublic transit network design is a very important part of urban transportation planning. In this paper, a new two-phase approach is proposed to design the bus-based park-and-ride system. In the first phase, park-and-ride facilities are located at the periphery of the traditional central business district. A new bus routing problem formulation is proposed to determine the optimal routes of urban bus fleet in the second phase. In the new formulation, park-and-ride facilities are modeled as depots, and each new urban bus route originates from a specific park-and-ride. Because the developed approach is non deterministic polynomial-time-hard and new to the literature, an efficient hybrid Genetic Algorithm and Ant Colony Optimization, referred to as GACO, is used to solve large-scale problems. At last, the developed approach is applied to design the bus-based park-and-ride system in Isfahan, Iran. Obtained results show the effectiveness of the developed approach.

Optimization of printed circuit board component placement using an efficient hybrid genetic algorithm

The hardware restrictions of surface mount placement machines, such as height, pick and place restrictions, and simultaneous pickup are often in printed circuit board (PCB)-related studies. This study proposes an efficient hybrid genetic algorithm (HGA) for solving the nozzle assignment problem and the component pick and place sequence problem. First, the proposed method obtains the sequence of the automatic nozzle changer (ANC) with the maximum number of simultaneous pickups and the minimum number of picks as the solution of the nozzle setup problem. Then, the proposed method uses the nearest neighbor search (NNS), 2-optimization, and a genetic algorithm (GA) with the known ANC sequences to obtain the PCB assembly time with the optimal component pick and place sequence. Experiments are conducted on the PCB of the EVEST EM-780 surface mount placement machine. Results show that the proposed HGA gives the lowest total number of picks, the shortest total head movement distance, and the minimum total PCB assembly time compared to those of other methods.

Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem

Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature.

Design of high-performing hybrid meta-heuristics for unrelated parallel machine scheduling with machine eligibility and precedence constraints

This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.

Improving Gas Allocation Optimization to a Group of Wells in Gas Lift Using an Efficient Hybrid Genetic Algorithm (HGA)

One of the most commonly used artificial lift methods in the petroleum industry is continuous gas lift. In continuous gas lift, gas at suitable pressure is injected in an appropriate depth into the tubing to gasify the oil column and, thus, facilitate the production. In gas lift, by increasing the gas injection rate into the well, the oil production rate enhances. At a point that is an optimal point of the well, by increasing gas injection rate, the oil production rate declines. At this point, the well produces the most oil. If there is no limitation in the total amount of available gas, a sufficient amount of gas could be injected into each well to get the maximum amount of production. However, often the total amount of available gas is insufficient to inject enough gas to an optimal point of each well and to reach the maximum oil production for each well. Therefore, allocating an optimum amount of gas to each well to obtain the field maximum oil production rate is necessary. In this work, a hybrid genetic algorithm was used to allocate the optimum amount of gas to a group of wells for three fields with a different number of wells. Based upon the fields' oil total production rate resulted from the gas allocation to the wells, the hybrid genetic algorithm shows better gas allocation to the wells in comparison with previous works with other methods.

An efficient hybrid genetic algorithm to design finite impulse response filters

Although genetic algorithms (GAs) have proved their ability to provide answers to the limitations of more conventional methods, they are comparatively inefficient in terms of the time needed to reach a repeatable solution of desired quality. An inappropriate selection of driving parameters is frequently blamed by practitioners. The use of hybrid schemes is interesting but often limited as they are computationally expensive and versatile. This paper presents a novel hybrid genetic algorithm (HGA) for the design of digital filters. HGA combines a pure genetic process and a dedicated local approach in an innovative and efficient way. The pure genetic process embeds several mechanisms that interact to make the GA self-adaptive in the management of the balance between diversity and elitism during the genetic life. The local approach concerns convergence of the algorithm and is highly optimized so as to be tractable. Only some promising reference chromosomes are submitted to the local procedure through a specific selection process. They are more likely to converge towards different local optima. This selective procedure is fully automatic and avoids excessive computational time costs as only a few chromosomes are concerned. The hybridization and the mechanisms involved afford the GA great flexibility. It therefore avoids laborious manual tuning and improves the usability of GAs for the specific area of FIR filter design. Experiments performed with various types of filters highlight the recurrent contribution of hybridization in improving performance. The experiments also reveal the advantages of our proposal compared to more conventional filter design approaches and some reference GAs in this field of application.

Optimization of Gas Allocation to a Group of Wells in Gas Lift in One of the Iranian Oil Fields Using an Efficient Hybrid Genetic Algorithm (HGA)

A hybrid genetic algorithm (HGA) was introduced to allocate optimum amount of gas. This method was applied to a group of wells in gas lift in the case of availability limited amount of gas. For testing the ability of the proposed HGA, the results of this work with those of previous works in a field with six wells were compared. Then for an Iranian southern west oil field with nine wells, gas allocation is performed using different amount of available gas. The results show that the introduced method (HGA) is very efficient tool in gas allocation issue.