Adaptive Hybrid Genetic Algorithm Research Articles

Task Reallocating for Responding to Design Change in Complex Product Design

Abstract In the real-world complex product design (CPD) process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing executing time strategy (CETS) is proposed in CPD process, where the executing time can be controlled with a non-linear compression cost. When design change occurs during the executing, task-resource reallocating is required to absorb the interference effects. Reallocating implies an increase in design cost and system instability; the proposed method CETS can address this issue effectively. CETS considers three objectives: completing time, stability, and change-adaptation cost. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve this mathematical method. The computational results of specific simulation examples verify the superiority. It shows that CETS is sensitive to design change, and the proposed algorithm AMOGATS can be effective to achieve the allocating by coordinating the objective consistency.

A reacting approach for design task allocating under resource unavailability

Resource unavailability (RU) is inevitable in complex product design (CPD) process, therefore, it is quite difficult to achieve an optimal solution. Based on the controllable characteristic of execution time, a multi-objective model with compressing execution time strategy (CETS) is considered in CPD. There are two stages in this model, in the first stage, we obtain an original allocation with the goal of minimizing completion time. In the second stage, CETS is utilized in response to RU considering the trade-off between compensatory cost and stability. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve the first mathematical step, CETS is performed by a basic genetic algorithm (GA). The computational results verify the superiority of hybrid genetic algorithm and proposed strategy.

폐쇄루프 공급망 모델 최적화를 위한 적응형혼합유전알고리즘 접근법

본 연구에서는 적응형혼합유전알고리즘(Adaptive Hybrid Genetic Algorithm: AHGA) 접근법을 이용한 폐쇄루프 공급망(Closed-Loop Supply Chain: CLSC) 모델 최적화를 다루고 있다. CLSC 모델 구축을 위해 공급업체(Part Supplier), 제품제조업체(Product Manufacturer)등으로 구성된 전방향물류(Forward Logistics)와 수집업체(Collection Center), 회복센터(Recovery Center)등으로 구성된 역물류(Reverse Logistics)를 함께 고려하고 있다. 제안된 CLSC 모델은 수리모형(Mathematical Model)으로 표현되며, AHGA접근법을 이용해 이행되어 그 최적해를 구하게 된다. 수치실험에서는 기존연구에서 제안된 몇몇 접근법과 AHGA 접근법을 함께 사용하여 그 수행도를 비교분석하였다. The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

An adaptive hybrid genetic algorithm for pavement management

The pavement maintenance and rehabilitation (M&R) strategy selection problem is an exceedingly hard problem to solve optimally. In this paper, a novel Adaptive Hybrid Genetic Algorithm (AHGA) is proposed which incorporates Local Search (LS) techniques into Genetic Algorithms (GA) to improve the overall efficiency and effectiveness of the search. Specifically, it contains two dynamic learning mechanisms to guide and combine the exploration and exploitation search processes adaptively. The first learning mechanism aims to assess the worthiness of conducting an LS reactively and to control the computational resources allocated to the application of this search technique efficiently. The second learning mechanism uses instantaneously learned probabilities to select from a set of pre-defined LS operators which compete against each other for selection which is the most appropriate at any particular stage of the search to take over from the evolutionary-based search process. The new AHGA is compared to a non-hybridized version of the GA by applying the algorithms to several case studies in order to determine the best pavement M&R strategy that minimizes the present value of the total M&R costs. The results show that the proposed AHGA statistically outperforms the traditional GA in terms of efficiency.

A Routing Optimization Adaptive Hybrid Genetic Algorithm Research

A Routing Optimization Adaptive Hybrid Genetic Algorithm Research

Method for Choosing a Balanced Set of Fault Tolerance Techniques for Distributed Computer Cystems

In the paper we consider a method for a reliability allocation problem (RAP) of distributed computer systems (DCS) under cost constraints. In this problem we maximize reliability of DCS under constraints of system cost. The article describes considered fault tolerance mechanisms. The mathematical formulation of RAP is provided. RAP is widely discussed in the literature. A detailed description of the method is ensured. The applied method is an evolutionary algorithm with an adaptive logic control procedure. The adaptive logic control procedure analyzes the results of evolutionary algorithm work in each generation and, based on this information, adjusts parameters. The key feature of the proposed method is the use of an adaptive hybrid genetic algorithm. The results of experiments with the implemented method are presented. This method was implemented as a pilot system which works in cooperation with DYANA simulation environment. Finally, future plans for the development of the presented method and tools are briefly described.

Hybridizing Adaptive Genetic Algorithm with Chaos Searching Technique for Numerical Optimization

Genetic algorithm (GA) is a population-based approach for heuristic search in optimization problems based on the principle of biologic evolution and natural selection. In this paper, we present a hybrid adaptive genetic algorithm with chaos searching technique for numerical optimization. On the one hand, two sets of crossover and mutation rates are for- mulated to automatically maintain the balance between exploration and exploitation during the genetic search process. On the other hand, the chaos searching technique is introduced into the adaptive genetic algorithm based on the decision mechanism for premature conver- gence adopted in this paper, whose main goal is to avoid being trapped into the local opti- mum. In addition, half of the total evolutionary generation is utilized as one of the decision conditions so as to speed up the convergent process. To validate the effectiveness and efficiency of the proposed approach, we apply it to four benchmark functions obtained from the literature, and the experimental results show that the proposed algorithm can find global optimal or the closer-to-optimal solutions and have faster search speed as well as higher convergence rate.

A Syntax Parsing Method Based on Adaptive Genetic Annealing Optimization HMM

In order to further enhance the performance of syntax parsing, for the shortcomings of hidden Markov model (HMM) in the parameter optimization, an improved syntax parsing method based on adaptive genetic annealing and HMM was presented. First, an adaptive hybrid genetic annealing algorithm was adopted to optimize HMM initial parameters. Second, the improved HMM was trained by Baum Welch algorithm, and then a modified Viterbi algorithm was used to recognize various types of phrases at the same layer, finally a hierarchical analysis algorithm and Viterbi algorithm were combined together to solve hierarchy and recursion in the sentence. In the adaptive genetic annealing HMM algorithm, genetic operators and parameters of simulated annealing (SA) were first respectively improved, subpopulations were classified according to the adaptive crossover and mutation probability of GA in order to realize the multi-group parallel search and information exchange, which could avoid premature and accelerate convergence, then SA was taken as a GA operator to strengthen the local search capability. Compared with several new approaches, Fβ = 1 value is averagely increased by 3%. The experiment results prove that this method is very effective for syntactic parsing.

Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm | fmls, rcrc, temp | Cmax problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.

Multiproject Resources Allocation Model under Fuzzy Random Environment and Its Application to Industrial Equipment Installation Engineering

This paper focuses on a multiproject resource allocation problem in a bilevel organization. To solve this problem, a bilevel multiproject resource allocation model under a fuzzy random environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the company manager aims to allocate the company's resources to multiple projects to achieve the lowest cost, which include resource costs and a tardiness penalty. On the lower level, each project manager attempts to schedule their resource-constrained project, with minimization of project duration as the main objective. In contrast to prior studies, uncertainty in resource allocation has been explicitly considered. Specifically, our research uses fuzzy random variables to model uncertain activity durations and resource costs. To search for the optimal solution of the bilevel model, a hybrid algorithm made up of an adaptive particle swarm optimization, an adaptive hybrid genetic algorithm, and a fuzzy random simulation algorithm is also proposed. Finally, the efficiency of the proposed model and algorithm is evaluated through a practical case from an industrial equipment installation company. The results show that the proposed model is efficient in dealing with practical resource allocation problems in a bilevel organization.

A Hybrid Algorithm for Open Vehicle Routing Optimization of Coal Mines Material

This study primarily focuses on solving a practical OVRP by applying a novel hybrid adaptive Genetic Algorithm and Tabu Search (GA-TS). The GA-TS combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. In the GA-TS Algorithm, individuals of population do TS local search with a certain degree of probability using natural number coding. It not only ensures global optimization, but also improves the speed of computing. The Algorithm is applied to optimize the mines material transport vehicle routing of China Zhengzhou Coal Mine and power Supply Co., Ltd. Through the applications, the optimal economical transport routing has been set up and economic costs have been greatly reduced.

Constrained Optimization Solution Based on an Improved Genetic Algorithm

A hybrid adaptive genetic algorithm is proposed for solving constrained optimization problems. The algorithm combines adaptive penalty method and smoothing technique in order to get no parameter tuning and easily escaping from the local optimal solutions. Meanwhile, local line search technique is introduced and a new crossover operator is designed for getting much faster convergence. The performance of the algorithm is tested on thirteen benchmark functions and the results indicate that the proposed algorithm is robust and effective.

A game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturing

This study seeks to address an approach for generating optimal process plans for multiple jobs in networked manufacturing. Because of production flexibility, generating several feasible process plans for each job is possible. Concerning the networked manufacturing mode, the specific scenario of competitive relationships, like delivery time existing between different jobs, should be taken into account in generating the optimal process plan for each job. As such, in this study, an N-person non-cooperative game-theoretic mathematical solution with complete information is proposed to generate the optimal process plans for multiple jobs. The game is divided into two kinds of sub-games, i.e. process plan decision sub-game and job scheduling sub-game. The former sub-game provides the latter ones with players while the latter ones decide payoff values for the former one to collaboratively arrive at the Nash equilibrium (NE). Endeavouring to solve this game more efficiently and effectively, a two-level nested solution algorithm using a hybrid adaptive genetic algorithm (HAGA) is developed. Finally, numerical examples are carried out to investigate the feasibility of the approach proposed in the study.

An intelligent computing technique for fluid flow problems using hybrid adaptive neural network and genetic algorithm

A new hybrid adaptive neural network (ANN) with modified adaptive smoothing errors (MASE) based on genetic algorithm (GA) employing modified adaptive relaxation (MAR) are presented in this paper to construct learning system for complex problem solving in fluid dynamics. This system can predict an incompressible viscous fluid flow represents by stream function ( ψ) through symmetrical backward-facing steps channels. The proposed learning system is constructed as an intelligent computing technique by enforcing three stages run simultaneously; the first stage concerns to construct finite-element method (FEM) employing a new approach named modified adaptive incremental loading (MAIL) to build-up in run-time a dataset driven that contains an effective patterns represented by ψ for specific Reynolds number ( Re), these patterns are associated to three kinds of clusters. The second stage is pertained a new hybrid neural network with new modification of adaptive smoothing errors and the third stage illustrated to modifying the numerical values of neural network connection weights through certain training algorithm with new optimization approach. The present simulation results of the proposed learning system are in good agreement with the available previous works and it is fast enough and stable.

A high-performance approach on mechanism isomorphism identification based on an adaptive hybrid genetic algorithm for digital intelligent manufacturing

The graph theory is an important method to achieve conceptual design for mechanism. During the process of kinematic structures enumeration using graph theory, isomorphism identification of graphs is an NP complete problem. It is important to improve the isomorphism identification efficiency and reliability. To solve the problem, an adaptive hybrid genetic algorithm is presented by mixing the improved genetic algorithm and local search algorithm. The crossover rate and mutation rate can be designed as adaptive parameters. Hence, the crossover rate and mutation rate can sustain the variety of the population and adjust the evolution. In the meantime, the pseudo-crossover operator is introduced to improve the search efficiency. In the last, some examples are illustrated to show the high efficiency of the algorithm by comparing with the results in other literatures.

Robust global and local search approach to resource-constrained project scheduling

The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in construction scheduling applications, in which optimal solutions are of great value to project planners. This paper presents a new adaptive hybrid genetic algorithm search simulator (AHGASS) for finding an optimal solution to the problem, and provides the strategies and practical procedures to develop the algorithm. Elitist genetic algorithm (EGA) developed is used for the global search, while random walk algorithm for the local search is incorporated into the EGA to overcome the drawbacks of general genetic algorithms, which are computationally intensive and premature convergence to a local solution. Computational experiments are presented to demonstrate the performance and accuracy of AHGASS. The proposed algorithm provides a comparable and competitive performance compared with the existing genetic algorithm (GA) hybrid heuristic methods. The findings demonstrate that AHGASS has significant promise for solving a large-sized RCPSP.

Adaptive Hybrid Genetic Algorithm for Technical Loss Reduction in Distribution Networks Under Variable Demands

In power distribution networks the load varies within any given time frame. It may, therefore, seem that a good approach to reduce losses would be the solving of a network reconfiguration problem to suit each of the significant load variations. However, frequent changes in configuration can trigger outages or cause transient problems; they are best avoided. A recent formulation of this problem explicitly considers load variations and proposes to restrain frequent reconfigurations by assuming that network topologies will remain unchanged for a given planning period. This formulation leads to a much larger optimization problem than that traditionally used for network reconfiguration; moreover, it requires a new approach to optimization which is capable of dealing with energy flows instead of only instantaneous power flows. Such an approach is proposed in this paper, which discusses the design of an adaptive hybrid genetic algorithm that fulfills these new requirements. Key concepts in evolutionary computation and analysis of distribution systems are explored to develop this new algorithm. Application to real case studies certifies its benefits.

Application of Genetic Algorithms for a Tyre Production Scheduling Information System

A optimization scheduling problem in a enterprise of manufacturing tyres is discussed in this paper. And this problem is reduced to a single machine scheduling problem to minimize setup times with batchs setup time depending on sequence. A method for solving tyre production scheduling problem using an effective adaptive hybrid genetic algorithm (AHGA) is proposed. We advance a novel operator (looping & cutting operator) to improve the mountain climbing ability of the genetic algorithm, and put forward adaptive probabilities of crossover and mutation based on information entropy. Computational results show that the proposed adaptive hybrid genetic algorithm is effective and robust.

Adaptive Hybrid Genetic Algorithm Parallel Interference Cancellation High Rate Multi-User Detection for Dual Rate W-CDMA Mobile Communications

Adaptive hybrid genetic algorithm concatenated with improved parallel interference cancellation, i.e. adaptive hybrid genetic algorithm parallel interference cancellation (AHGAPIC) was proposed. A study is conducted on the application of AHGAPIC to soft decoding high rate multi-user detection with diversity reception for dual-rate wideband DS-CDMA spread spectrum communications, aiming to mitigate the effect of multiple access interference. The relevant research has revealed that the local search capability of hybrid genetic algorithm (HGA) is still not good enough. Therefore, first, two evolutionary operations, i.e. inversion and insertion are merged into HGA to constitute a novel algorithm. With its moderate local search capability, this new algorithm can search for the global optimum region according to the information entropy, and then it is made adaptively vary its probabilities of crossover and mutation depending on the fitness values of the solutions to form the adaptive hybrid genetic algorithm (AHGA). Second, AHGA is utilized to effectively identify the better and better binary string to maximize the log-likelihood function of dual-rate multi-user detection. As AHGA converges to the optimum region, the control factor of the improved parallel interference cancellation (IPIC) detector is set to be the ratio of the average fitness value to the maximum fitness value of the population of AHGA. Finally, equipped with both the control factor and the binary string with the maximum fitness value as the initial data, the IPIC detector can rapidly find out the approximately optimum soft decoding vector. Then, it can obtain the approximately global optimum estimate point on the basis of the soft decoding rule, corresponding to the transmitted data bits. A lower bound of computational complexity has been achieved through simulations and qualitative analyses. The property of the proposed algorithm to converge rapidly leads to lower computational complexity. Emulation results have shown that the AHGAPIC soft decoding high rate multi-user detector is superior to other suboptimum detectors considered in this paper in terms of two points. They are the mitigation of multiple access interference and the resistance to near-far effects. Its performance is close to the sequential group optimum multi-user detector but with a shorter time delay.

Development of the enhanced self‐adaptive hybrid genetic algorithm (e‐SAHGA)

Genetic algorithms allow solution of more complex, nonlinear groundwater remediation design problems than traditional gradient‐based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. This technical note focuses on the development and application of a new HGA, the enhanced self−adaptive hybrid genetic algorithm (e‐SAHGA), which is an enhancement of a previously developed HGA called SAHGA. The application of the e‐SAHGA algorithm to a hypothetical groundwater remediation design problem showed 90% reliability in identifying the optimal solution faster than the SGA, with average savings of 64% across 100 random initial populations. These results are considerably improved over SAHGA, which attained only 80% reliability and 14% average savings on the same initial populations.