Mutation Operator Of Genetic Algorithm Research Articles

A unified algorithm based on HTS and self-adapting PSO for the construction of octagonal and rectilinear SMT

The Steiner minimal tree (SMT) problem is an NP-hard problem, which is the best connection model for a multi-terminal net in global routing problem. This paper presents a unified algorithm for octagonal and rectilinear SMT construction based on hybrid transformation strategy (HTS) and self-adapting particle swarm optimization. Firstly, an effective HTS is proposed to enlarge the search space and improve the convergence speed. Secondly, the proposed HTS in the evolutionary process may produce an ineffective solution, and consequently the crossover and mutation operators of genetic algorithm (GA) based on union-find sets is proposed. Thirdly, a self-adapting strategy that can adjust the acceleration coefficients is proposed to further improve the convergence and the quality of the proposed algorithm. Finally, the hybrid transformation can be applied to GA and the proposed algorithm can be applied to rectilinear architecture. To our best knowledge, the proposed algorithm is the first unified algorithm to solve the SMT construction under both octagonal and rectilinear architecture. The experimental results show that the proposed algorithm can efficiently provide a better solution for SMT problem both in octagonal and rectilinear architectures than others. Moreover, the algorithm can obtain several topologies of SMT, which is beneficial for optimizing congestion in VLSI global routing stage.

An enhanced hybridized artificial bee colony algorithm for optimization problems

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm

In this paper, a new version of shuffle frog leaping algorithm (SFLA) which called modified SFLA (MSFLA) is proposed. To drive the proposed method both local and global search mechanisms in the original SFLA are modified. The local search mechanism is modified by introducing the movement inertia equation of particle swarm optimization (PSO). Furthermore, the global search mechanism is modified using the crossover and mutation operators of genetic algorithm (GA). Also, the formulation of the combined heat, emission and economic dispatch (CHEED) problem considering the availability of wind and solar power is presented where the objective functions of the CHEED problem are converted into a single objective function using price penalty function. Then, the proposed MSFLA is employed to solve the problem of CHEED with wind and solar power. To show the effectiveness of the proposed MSFLA, different test systems with different scenarios are used. In addition, the scalability of the proposed MSFLA is tested using large-scale test systems. The results of the proposed MSFLA are compared with those obtained by other heuristic and non-heuristic published methods. The comparison with other methods proves the efficiency and the superiority of the proposed MSFLA over other methods in all scenarios.

An EDA-GA Hybrid Algorithm for Multi-Objective Task Scheduling in Cloud Computing

As one of the hot issues in cloud computing, task scheduling is an important way to meet user needs and achieve multiple goals. With the increasing number of cloud users and growing demand for cloud computing, how to reduce the task completion time and improve the system load balancing ability have attracted increasing interest from academia and industry in recent years. To meet the two aforementioned goals, this paper develops an EDA-GA hybrid scheduling algorithm based on EDA (estimation of distribution algorithm) and GA (genetic algorithm). First, the probability model and sampling method of EDA are used to generate a certain scale of feasible solutions. Second, the crossover and mutation operations of GA are used to expand the search range of solutions. Finally, the optimal scheduling strategy for assigning tasks to virtual machines is realized. This algorithm has advantages of fast convergence speed and strong search ability. The algorithm proposed in this paper is compared with EDA and GA via the CloudSim simulation experiment platform. The experimental results show that the EDA-GA hybrid algorithm can effectively reduce the task completion time and improve the load balancing ability.

Optimal design of fighter aircraft wing panels laminates under multi-load case environment by ply-drop and ply-migrations

The ply-drop (PD) is termination of specific plies at rib-axis for getting tapered laminates. The present optimization study aims to achieve minimum weight tapered wing panels laminates by PD followed by ply-migrations (PM). The PM are required for ply-continuity (blending) and achieving smooth external aerodynamic surface. A genetic-algorithm mutation operator and fitness based search algorithm is developed in the present study for the optimization. The laminate weight minimization has been achieved as goal of multi-objective optimization (MOO), by utilizing excess design margins of Tsai-Wu first ply failure-index (FI) and wing tip lateral deflection. The finite-element (FE) model of laminate is a set of discrete laminates (chromosomes) between ribs with continuity by virtue of ply-orientations. To select best fit laminate, ply orientations were randomly selected and perturbed for thickness during optimization. The fitness function for evaluating chromosomes is a composite function of multi-objective design requirements and design constraints. The algorithm submits orientation/thickness combinations to ABAQUS/CAE by python-script for function evaluation. The application of algorithm over an initially assumed quasi-isotropic laminate of uniform thickness showed 57% weight reduction for a fighter aircraft’s wing panel. The optimization process is automated making PD practically viable in the design process itself.

Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure

This paper presents a model to deal with reliability allocation problem of phased mission systems (PMS), especially for PMS with uncertainty in components’ parameters due to inaccurate information and different phase environments. In practice, real value of the reliability of a component may become lower than its designed value due to such uncertainty, thus making the whole system fail to meet the required reliability level. Therefore, in this paper, we present a model that incorporates the component uncertainty in the system reliability allocation process and then propose a variance-based global importance hybrid heuristic algorithm for its solution. The variance-based global importance measure is used to evaluate the importance of a component to the mission reliability of PMS while the reliabilities of all components vary randomly. The main procedures of the proposed algorithm include: (1) generate feasible solutions by roulette wheel selection method based on a global importance index; (2) improve solutions by adjusting reliability parameter values of components according to their global importance; and (3) improve solutions by crossover and mutation operations of genetic algorithm (GA). To illustrate the effectiveness of the proposed model and algorithm, two examples of PMS are presented and the allocation solutions are validated through Monte–Carlo simulation method. Finally, we compare the proposed model with a general allocation model that does not consider component uncertainty, and additionally with a cost-based heuristic algorithm and a particle swarm optimization (PSO) algorithm. Our results show that component uncertainty has significant influence on the confidence level that an allocation solution satisfies the required system reliability. Hence, it is essential to consider component uncertainty in reliability allocation process. In comparison with the cost heuristic algorithm and the PSO algorithm, the proposed algorithm is more effective in reliability allocation of PMS with uncertainty in components’ parameters.

Prediction of electricity demand of China based on the analysis of decoupling and driving force

The emerging complex circumstances caused by the essence of the new economic normal and the low-carbon development of power industry lead to the demand for electricity from the rapid growth phase to a `new normal' pace for China. To better capture the more and more non-linear and non-stationary characteristics of electricity demand, Tapio decoupling model is applied to research the decoupling of electricity demand from economic growth, and T 's correlation degree is used to measure the influencing degree and reveal the role direction of each driver on electricity demand. On these bases, a novel improved particle swarm optimisation-extreme learning machine (IPSO-ELM) hybrid forecasting model is proposed to predict electricity demand. In IPSO-ELM, a mutation operator of genetic algorithm is introduced into standard PSO to ensure the overall convergence and enhance the accuracy of convergence, and the improved PSO is adopted to optimise the input weights and hidden bias of traditional ELM which are randomly generated. Finally, the case study of China demonstrated the efficacy and feasibility of IPSO-ELM. Simultaneously, the comparisons with ELM, back propagation neural network, support vector machine, GM (1, 1), and logistic model showed that IPSO-ELM has a better forecasting performance in electricity demand.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach

In recent years, due to environmental concerns, remanufacturing of products is practiced in different companies and closed loop supply chain network in these companies is significant to optimize. Therefore, current study is aimed to determine an optimal closed loop supply chain network, which is composed of multiple producers, remanufacturers, intermediate centers and customer centers. Furthermore, uncertainty in the demand and uncertainty in the quantity of returned products is considered simultaneously in the network to make it significantly useful in the uncertain environment. A novel genetic artificial bee colony (GABC) algorithm is introduced with a new food source representation for the current problem. The proposed GABC algorithm considered neighbor food sources for local search and used crossover and mutation operations of genetic algorithm to enhance the exploration ability of the proposed algorithm. Taguchi method is employed to compute the optimal parameters of GABC for two different size test problems taken from literature and a Case problem which are modified according to the current research problem. The performance of presented GABC algorithm is tested by comparing the results of considered test problems with the results obtained from the original artificial bee colony (ABC) algorithm and genetic algorithm (GA). Moreover, to test the robustness of the proposed GABC algorithm, different scenarios of small and large size problems based on the quantity of demand and variations in demand are made to perform the experiments. Results indicate that proposed GABC outperforms standard ABC and GA in different scenarios to give smaller value of the total cost of network and gives more robust results to give smaller variations in the total cost of network due to uncertain variations in the demand, as compared to original ABC and GA.d

Research of Resource Scheduling based on ACA-GA in the Cloud Computing

How to better conduct research resource scheduling has long been a research direction of cloud computing. This paper, aiming at slow convergence and easiness of falling local optimum of ant colony algorithm,has integrated genetic algorithm into the ant colony algorithm and obtained hybrid algorithm (ACA -GA); in the initial solution of the ant colony algorithm, it has adopted selection, crossover and mutation operations of genetic algorithm to obtain an effective initial solution; secondly, it has used the perception threshold of ant colony algorithm path setting to regulate individual selection optimal path; finally, it has improved volatile factor so as to significantly improve the updating efficiency of pheromone. The algorithm in the paper proved that the performance of the algorithm has been also significantly improved through classical test functions. Cloudsim platform shows that, the algorithm above mentioned reduces the time and cost spent in resource scheduling of, hence has some promotional value.

The Fuzzy Optimization Algorithm to Solve Customer’s Highest Satisfaction Under Fuzzy Time

The fuzzy completion time and customer request are two main uncertain factors in the process of the actual workshop production. The scheduling model under uncertain situation based on fuzzy mathematics is established in this paper. The average customer satisfaction acts as the objective of the model. Next, a novel hybrid algorithm is obtained through improving the mutation operation of genetic algorithm on the basis of simulated annealing algorithm. The customers can determine the priority through the process time of the process route to evaluate the effectiveness of the process route. The effectiveness and superiority of the proposed algorithm are verified through the contrast experiment with the existing algorithms.

A metaheuristic optimization framework for informative gene selection

Abstract This paper presents a metaheuristic framework using Harmony Search (HS) with Genetic Algorithm (GA) for gene selection. The internal architecture of the proposed model broadly works in two phases, in the first phase, the model allows the hybridization of HS with GA to compute and evaluate the fitness of the randomly selected solutions of binary strings and then HS ranks the solutions in descending order of their fitness. In the second phase, the offsprings are generated using crossover and mutation operations of GA and finally, those offsprings were selected for the next generation whose fitness value is more than their parents evaluated by SVM classifier. The accuracy of the final gene subsets obtained from this model has been evaluated using SVM classifiers. The merit of this approach is analyzed by experimental results on five benchmark datasets and the results showed an impressive accuracy over existing feature selection approaches. The occurrence of gene subsets selected from this model have also been computed and the most often selected gene subsets with the probability of [0.1–0.9] have been chosen as optimal sets of informative genes. Finally, the performance of those selected informative gene subsets have been measured and established through probabilistic measures.

A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown

As the research interest in distributed scheduling is growing, distributed permutation flowshop scheduling problems (DPFSPs) have recently attracted an increasing attention. This paper presents a fuzzy logic-based hybrid estimation of distribution algorithm (FL-HEDA) to address DPFSPs under machine breakdown with makespan criterion. In order to explore more promising search space, FL-HEDA hybridises the probabilistic model of estimation of distribution algorithm with crossover and mutation operators of genetic algorithm to produce new offspring. In the FL-HEDA, a novel fuzzy logic-based adaptive evolution strategy (FL-AES) is adopted to preserve the population diversity by dynamically adjusting the ratio of offspring generated by the probabilistic model. Moreover, a discrete-event simulator that models the production process under machine breakdown is applied to evaluate expected makespan of offspring individuals. The simulation results show the effectiveness of FL-HEDA in solving DPFSPs under machine breakdown.

A Self-Driven and Adaptive Adjusting Teaching Learning Method for Optimizing Optical Multicast Network Throughput

AbstractWith the development of one point to multiple point applications, network resources become scarcer and wavelength channels become more crowded in optical networks. To improve the bandwidth utilization, the multicast routing algorithm based on network coding can greatly increase the resource utilization, but it is most difficult to maximize the network throughput owing to ignoring the differences between the multicast receiving nodes. For making full use of the destination nodes’ receives ability to maximize optical multicast’s network throughput, a new optical multicast routing algorithm based on teaching-learning-based optimization (MR-iTLBO) is proposed in the paper. In order to increase the diversity of learning, a self-driven learning method is adopted in MR-iTLBO algorithm, and the mutation operator of genetic algorithm is introduced to prevent the algorithm into a local optimum. For increasing learner’s learning efficiency, an adaptive learning factor is designed to adjust the learning process. Moreover, the reconfiguration scheme based on probability vector is devised to expand its global search capability in MR-iTLBO algorithm. The simulation results show that performance in terms of network throughput and convergence rate has been improved significantly with respect to the TLBO and the variant TLBO.

An Improved PSO Algorithm Based on Mutation Operator and Simulated Annealing

Particle swarm optimization (PSO) algorithm is simple stochastic global optimization technique, but it exists unbalanced global and local search ability, slow convergence speed and solving accuracy. An improved simulated annealing (ISAM) algorithm is introduced into the PSO algorithm with crossover and Gauss mutation to propose an improved PSO (ISAMPSO) algorithm based on the mutation operator and simulated annealing in this paper. In the ISAMPSO algorithm, the mutation operator of genetic algorithm is introduced into the SA algorithm as a generation mechanism of new solution in order to propose an improved simulated annealing algorithm with mutation (ISAM). Then the ISAM algorithm is introduced into the PSO algorithm to jump out the local optimum, effectively achieve the global optimum adjust and optimize the population, maintain the diversity of the population, improve the local search ability and convergence speed. Six classical functions are selected to test the performance of the proposed ISAMPSO algorithm. The simulation experiments results show that the proposed ISAMPSO algorithm can effectively overcomes the stagnation phenomenon and enhance the global search ability. The convergence speed and accuracy were better than the PSO algorithm.

A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation

An important factor for efficiently managing the supply chain is to efficiently control the physical flow of the supply chain. For this purpose, many companies try to use efficient methods to increase customer satisfaction and reduce costs. Cross docking is a good method to reduce the warehouse space requirements, inventory management costs, and turnaround times for customer orders. This paper proposes a novel dynamic genetic algorithm-based method for scheduling vehicles in cross docking systems such that the total operation time is minimized. In this paper, it is assumed that a temporary storage is placed at the shipping dock and inbound vehicles are allowed to repeatedly enter and leave the dock to unload their products. In the proposed method of this paper two different kinds of chromosome for inbound and outbound trucks are proposed. In addition, some algorithms are proposed including initialization, operational time calculation, crossover and mutation for inbound and outbound trucks, independently. Moreover a dynamic approach is proposed for performing crossover and mutation operation in genetic algorithm. In order to evaluate the performance of the proposed algorithm of this paper, various examples are provided and analyzed. The computational results reveal that the proposed algorithm of this paper performs better than two well-known works of literature in providing solutions with shorter operation time.

Cryptosystem for Providing Secured Application based on Genetic Algorithm

In this study, has shown how to design asymmetric encryption algorithm based on using two pseudorandom number generators and the features of crossover and mutation operations of Genetic algorithm (GA).the roles of pseudorandom number generators, in the proposed algorithm, help to generate dynamic representation for each character and define where to apply the crossover and mutation operations in deterministic way to produce the ciphertext; The proposed encryption algorithm solves the problem of exchange and distribution of private keys over the networks by using crossover operator; The various examples and implementation of the algorithm proves that it exchanges the keys and encrypts successfully all the characters, and serves the goals of cryptography.

Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm

This paper reports a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. A mathematical model is established to illustrate the sensor network optimization based on fault-sensor dependence matrix. The crossover and mutation operators of genetic algorithm (GA) are introduced into the update strategy of shuffled frog leaping algorithm (SFLA) and a new ID-SFLA is systematically developed. Numerical simulation results show that the ID-SFLA has an excellent global search ability and outstanding convergence performance. The ID-SFLA is applied to the sensor's optimal selection for a gearbox. In comparison with GA and discrete shuffled frog leaping algorithm (D-SFLA), the proposed ID-SFLA not only poses an effective solving method with swarm intelligent algorithm, but also provides a new quick algorithm and thought for the solution of related integer NP-hard problem.

An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification.

A study on a cooperative character modeling based on an improved NSGA II

Different animation characters are loved by people at different ages, in different countries and from different background, which implies the difficulties of designing characters that might be loved by many. Grouping different designers together by computer networks or the Internet is surely a good solution to design a character with expected popularity. This paper presents a character modeling method based on an improved non-dominated sorting genetic algorithm II (CMIN). CMIN borrows ideas from biological evolution, especially from multi-objective genetic algorithm (MOGA) and is formalized as a procedure for character modeling. CMIN adopts binary tree data structure to express transformation rules which are used to diversify character models and uses crossover and mutation operators of genetic algorithm to generate new rules. CMIN also adopts cooperative multi-objective evaluation on generated characters. The objectives are designed to embody both qualitative and quantitative aspects of character personalities, which are assigned by different cooperative designers and calculated automatically by computers respectively. The incorporation of qualitative and quantitative evaluation is formally realized by introducing a MOGA framwork. A multi-objective evaluation-based cooperative character modeling system (MOCMS) was developed to verify the proposed CMIN. Representative case studies demonstrate that the proposed method can evolve character models according to the designers' intentions and preferences and generate creative character models far beyond man's own imagination.