Genetic Algorithm Based Hybrid Approach Research Articles

Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

In recent years, the researchers have got progressively more interest in wireless sensor networks (WSNs) as it plays a vital role in numerous applications. WSNs have strict resource constraints such as limited bandwidth, limited energy, limited processing capability, and limited memory. Among them, the most critical one is limited energy. So, energy efficiency has got significant importance in the WSNs. Energy consumption is mainly due to computation and communication between sensor nodes and the base station. Energy consumption for communication is more when compared to computation. Hence it is indispensable to focus on designing an energy-efficient routing for WSN. In this paper, a cluster-based routing using simulated annealing and genetic algorithm-based hybrid (SAGA-H) approach has been proposed. The proposed approach is explained and simulated using MATLAB. Further, the results observed have been compared with existing GA-based approach with respect to network lifetime, the number of packets sent to BS and sink, and average residual energy. From the simulation results, it is observed that the proposed approach outperforms the existing GA-based approach.

Genetic algorithm-based hybrid approach for optimal instance selection of minimising makespan in permutation flowshop scheduling

Recently, the instance selection is getting more attention for the researchers to achieve enhanced performance of algorithms. A typical flowshop dataset can be represented in the form of a number of instances. The instances that are recorded during production process may not be a good example to learn useful knowledge. Therefore, the selection of high quality instances can be considered as a search problem and be solved by evolutionary algorithms. In this work, a genetic algorithm (GA) is proposed to select a sub-set of best instances. The selected instances are represented in the form of IF-Then else rules using a decision tree (DT) algorithm. The seed solution from DT is used as input to a scatter search (SS) algorithm for a few iterations, which acts as a local search to find the best value of the selected instances. The GA is used to select best instances in order to have a smaller tree size with good solution accuracy for minimizing makespan criterion in permutation flowshop scheduling. The computational experiments are performed with standard problems and compared against various existing literatures.

Genetic algorithm based hybrid approach to solve uncertain multi-objective COTS selection problem for modular software system

Genetic algorithm based hybrid approach to solve uncertain multi-objective COTS selection problem for modular software system

A Genetic Algorithm Based Hybrid Approach for Reliability-Redundancy Optimization Problem of a Series System with Multiple-Choice

The goal of this paper is to introduce an application of hybrid algorithm in reliability optimization problems for a series system with parallel redundancy and multiple choice constraints to maximize the system reliability subject to system budget and also to minimize the system cost subject to minimum level of system reliability. Both the problems are solved by using penalty function technique for dealing with the constraints and hybrid algorithm. In this algorithm, the well-known real coded Genetic Algorithm is combined with Self-Organizing Migrating Algorithm. As special cases, both the problems are formulated and solved considering single component without redundancy. Finally, the proposed approach is illustrated by some numerical examples and the computational results are discussed.

Genetic algorithm based hybrid approach to solve fuzzy multi-objective assignment problem using exponential membership function.

This paper presents a genetic algorithm based hybrid approach for solving a fuzzy multi-objective assignment problem (FMOAP) by using an exponential membership function in which the coefficient of the objective function is described by a triangular possibility distribution. Moreover, in this study, fuzzy judgment was classified using α-level sets for the decision maker (DM) to simultaneously optimize the optimistic, most likely, and pessimistic scenarios of fuzzy objective functions. To demonstrate the effectiveness of the proposed approach, a numerical example is provided with a data set from a realistic situation. This paper concludes that the developed hybrid approach can manage FMOAP efficiently and effectively with an effective output to enable the DM to take a decision.

A Genetic Algorithm based Hybrid Approach to Solve Multi-objectiveInterval Assignment Problem by Estimation Theory

Objectives: In industrial organization and management science, Assignment Problem (AP) is most studied problem with multiple uncertain objectives; normally this uncertainty is represented by an interval. The main aim of this paper is to find a solution of such Multi-objective Interval Assignment Problem (MOIAP). Methods/Statistical analysis: This paper proposes a Genetic Algorithm (GA) based hybrid approach to solve MOIAP by using point and interval estimation theory. Estimation theory is used to manage such uncertainty with sampling. For that the samples are taken from the past data and find point estimation form the samples of each parameter to specified the range of interval with confidence level and this range is considered as an interval in AP. Findings: Traditional method like fuzzy, weighted min-max, goal programming is utilized by several researchers for find solution of MOIAP but here GA provided a proper analysis based solution. In this paper realistic example is provided to represents the solution effectiveness by estimation theory which shows that developed approach provide improved and analysis based solution compare to other approach.

Genetic Algorithm Based Hybrid Approach to Solve Optimistic, Most-likely and Pessimistic Scenarios of Fuzzy Multi-objective Assignment Problem Using Exponential Membership Function

Genetic Algorithm Based Hybrid Approach to Solve Optimistic, Most-likely and Pessimistic Scenarios of Fuzzy Multi-objective Assignment Problem Using Exponential Membership Function

Feature selection using a genetic algorithm-based hybrid approach

The present work proposes a hybrid feature selection model aimed at reducing training time whilst maintaining classification accuracy. The model includes adjusting a decision tree for producing feature subsets. Such subsets’ statistical relevance was evaluated from their resulting classification error. Evaluation involved using the k-nearest neighbors’ rule. Dimension reduction techniques usually assume an element of error; however, the hybrid selection model was tuned by means of genetic algorithms in this work. They simultaneously minimise the number of features and training error. Contrasting with conventional methods, this model also led to quantifying the relevance of each training set’s features. The model was tested on speech signals (hypernasality classification) and ECG identification (ischemic cardiopathy). In the case of speech signals, the database consisted of 90 children (45 recordings per sample); the ECG database had 100 electrocardiograph records (50 recordings per sample). Results showed average reduction rates of up to 88%, classification error being less than 6%.

Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling

The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with processing constraints that are more restrictive and a scheduling objective that is more general than those of the standard job-shop scheduling problem (JSSP). A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the problem in this paper. The GA is used for optimization of sequence and a neural network (NN) is used for optimization of operation start times with a fixed sequence. After detailed analysis of an expanded job shop, new types of neurons are defined to construct a constraint neural network (CNN). The neurons can represent processing restrictions and resolve constraint conflicts. CNN with a gradient search algorithm, gradient CNN in short, is applied to the optimization of operation start times with a fixed processing sequence. It is shown that CNN is a general framework representing scheduling problems and gradient CNN can work in parallel for optimization of operation start times of the expanded job shop. Combining gradient CNN with a GA for sequence optimization, a hybrid approach is put forward. The approach has been tested by a large number of simulation cases and practical applications. It has been shown that the hybrid approach is powerful for complex EJSSP.