Mutation Operator Of Genetic Algorithm Research Articles

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

Modeling and solving a multi‐objective optimal portfolio of upstream oil and gas assets

AbstractThis paper focuses on optimizing project investments in oil and gas companies. It proposes a multi‐objective method for investing in oil and gas assets, considering factors such as scale and efficiency. The model takes into account the presence of nonlinear equations and integer constraints, and establishes a nonlinear multi‐objective mixed integer programming portfolio model for oil and gas. The weights of multiple objectives are determined using support vector machines. The optimization model incorporates the displacement transfer concept of particle swarm optimizer and the mutation operation of genetic algorithm using the transfer strategy of Gaussian particle swarm. The effectiveness of the model and algorithm is demonstrated through two examples.

Robot path planning based on shuffled frog leaping algorithm combined with genetic algorithm

When the shuffled frog leaping algorithm (SFLA) is used to solve the robot path planning problem in obstacle environment, the quality of the initial solution is not high, and the algorithm is easy to fall into local optimization. Herein, an improved SFLA named ISFLA combined with genetic algorithm is proposed. By introducing selection, crossover and mutation operators in genetic algorithm, the ISFLA not only improves the solution quality of the SFLA, but also accelerates its convergence speed. Moreover, the ISFLA also proposes a location update strategy based on the central frog, which makes full use of the global information to avoid the algorithm falling into local optimization. By comparing ISFLA with other algorithms including SFLA in the map environment of different obstacles, it is confirmed that ISFLA can effectively improve the minimum path optimization and robustness in the simulation experiments of mobile robots.

Research on control strategy of asymmetric electro-hydraulic servo system based on improved PSO algorithm

In the electro-hydraulic servo control system, there are some problems, such as low position control accuracy, uncontrollable speed, speed impact, and asymmetric control due to the asymmetry of hydraulic cylinder. To solve the above problems, this paper proposes a Fuzzy PID control algorithm with load force compensation based on improved PSO optimization. This improved particle swarm optimization algorithm introduces crossover and mutation operations in genetic algorithm to improve the optimization performance of traditional particle swarm optimization algorithm. The mathematical model of valve controlled asymmetric cylinder is established, and the Amesim linearization analysis function is used to identify the system, establish the transfer function, and build the control system. The joint simulation and test results of Amesim and MATLAB show that the proposed control strategy can realize the simultaneous control of speed and position. Under different loads, different speeds, and different input follow signals, it does not need to adjust any parameters. Regardless of forward or reverse operation, the optimal dynamic performance and minimum steady-state error are obtained, which greatly improves the response speed of the system. The effectiveness and superiority of the improved PSO compound control strategy are verified.

Thunderstorm disaster prediction based on enhanced GWO optimized BP neural network

Aiming at the problem that traditional BP algorithm is prone to fall into local optimum when carrying out thunderstorm disaster prediction, a thunderstorm disaster prediction method based on BP neural network optimized by enhanced GWO algorithm is proposed in this paper. Firstly, the global search capability of GWO is enhanced by introducing clone mutation operation in genetic algorithm and position update idea in particle swarm optimization, and the convergence speed of the algorithm is improved. Then, the BP neural network weight and threshold optimized by the enhanced GWO algorithm are used to build the network model and predict the occurrence of thunderstorm disasters. Simulation results show that compared with the original BP algorithm, GWO-BP algorithm and PSO-BP algorithm, the improved IPSGWO-BP algorithm improves the accuracy of thunderstorm disaster prediction by 13.33%, 12.50% and 8.33%, respectively. Meanwhile, the null alarm rate is lower, the optimization ability is stronger, and the convergence speed is faster.

Transformer fault diagnosis method based on improved whale optimization algorithm to optimize support vector machine

An improved whale algorithm optimized support vector machine (SVM) for fault diagnosis of oil-immersed transformers is proposed. Firstly, based on the dissolved gas analysis (DGA) in oil, with no coding ratio method is utilized to extract oil can express the state of the transformer in 9 dimensional vector, will be the vector normalization and USES kernel principal component analysis (KPCA) for feature extraction, and reduce the dimension of feature vector, to avoid the overlapping between the information at the same time to speed up the speed of 5 s; Secondly, the selection, crossover and mutation operations of genetic algorithm (GA) were introduced into whale optimization algorithm (WOA), and the penalty factor and kernel parameters of SVM were further iterated. Finally, the DGA data of 627 cases of oil-immersed transformers were collected and input into GWOA-SVM, GA-SVM, PSO-SVM and WOA-SVM models, and the diagnosis results were 94.05%, 78.56%, 82.28% and 86.37%, respectively. At the same time, the stability test experiment was carried out with 10% fault data, and the diagnostic accuracy of GWOA-SVM model only decreased by about 5%. The results show that the model established in this paper has the characteristics of high diagnostic accuracy and strong stability.

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

Artificial Bee Colony Algorithm to Solve the Shortest Route Problem with Fuzzy Arc Weight

The shortest path problem usually assumes a clear value (crisp) for the weights of each route. However, the crisp weight sometimes ends up ambiguous in practice in the daily life. The number of arc weights is calculated using fuzzy logic, α-cut fuzzy numbers. The Artificial Bee Colony (ABC) algorithm that adopts bee behavior in food searching is used to solve the shortest path problem. This study discusses how to solve numerical problems to find the shortest path using the Artificial Bee Colony algorithm if the arc weights are fuzzy numbers. The algorithm starts with finding the initial solution using Algorithm 1 and then calculated each distance using the sum of α-cut methods. After that, do a local search for each initial solution using the genetic algorithm mutation operator, then searched the distance amount using the same way then compared using the result of distance . The next step was to calculate the fitness value of each solution that would be used to calculate the probability value. The final step was to improve the solution, and an improved solution is said to be a solution if it does not improve again anymore. The calculation process was done repeatedly from the second step to the maximum iteration, i.e. when iteration had reached the limit or the iteration limit fails. Based on the calculation process using ABC algorithm in the case of numerical example, delivery of clean water supply in Gunung Kidul Regency, obtained the shortest route, that is route 1,2,3,5,6 with interval distance equal to 459, 9142.

H-PSO Routing Optimization Model for Zoomlion Ghana Limited

This research combines Particle Swarm Optimization (PSO) with Crossover and Mutation Operators of Genetic Algorithm (GA) to produce a hybrid optimization algorithm to solve a routing problem identified at Zoomlion Ghana Limited, Sekondi Takoradi branch. PSO is known to converge prematurely and can be trapped into a local minimum especially with complex problems. On the other hand, GA is a robust and works well with discrete and continuous problems. The Crossover and Mutation operations of GA makes the iterations converges faster and are reliable. The hybrid algorithm therefore merges these operators into PSO to produce a more reliable optimal solution. The hybrid algorithm was then used to solve the routing problem identified at Zoomlion Ghana Limited, Sekondi Takoradi branch. A total of 160 public waste bin centers scattered in the metropolis and the distance between them were considered. The main aim was to determine the best combination of the set of routes connecting all the bin centers in the municipality that will produce the shortest optimal route for the study. MATLAB simulation was run of the list of distances to determine the optimal route. After 10,000 iterations, PSO produced an optimal result of 81.6 km, GA produced an optimal result of 88.9 km and the proposed hybrid model produced an optimal result of 79.9 km

Research on intelligent design method of ship multi-deck compartment layout based on improved taboo search genetic algorithm

This paper presents an improved taboo search genetic algorithm (ITSGA) for intelligent design of ship multi-deck compartment layout (SMCL). The optimization of ship multi-deck residential compartment layout belongs to the combinatorial optimization problem with various performance constraints which needs to consider the layout of function cabins, deck passages and stairways between decks and so on. In this paper, the optimization model for SMCL is established, which include the layout area model, the relative location model, the absolute location model and the ergonomic model. ITSGA is proposed to improve the local search ability of genetic algorithm (GA) by introducing the neighborhood transformation criterion and Taboo criterion of Taboo search algorithm into GA. Then a new coding method is given according to the characteristics of ship cabins layout problem to avoid the damage to the cabin sequence caused by crossover and mutation operations in GA. During the layout process, the energy method is firstly used to determine the deck layer of various cabins to be arranged, and then the position of deck passages, cabins, and stairways between upper and lower decks are carried out by nested ITSGA. Finally, the results of numerical simulation experiments demonstrate the feasibility and effectiveness of the established method.

An improved optimization method for materials distribution based on spatiotemporal clustering in automobile assembly lines

The development of smart factories has put forward more flexible logistics needs for automobile assembly system, and efficient scheduling strategies to meet these requirements still demand prompt solution. Thus, this paper focuses on the problem of materials distribution with automated guided vehicles (AGVs) in automobile assembly lines. The mathematical model is established in the light of actual situation with mixed time windows and an improved genetic algorithm (GA) is developed. Considering the demand characteristics both in time and space, material demand points are clustered based on their spatiotemporal distance to generate the initial population. Then, selection, crossover and mutation operators of GA are also ameliorated as necessary to minimize the total travel cost. Finally, practical examples are carried out to demonstrate the effectiveness of this methodology.

Vehicles robust scheduling of hazardous materials based on hybrid particle swarm optimisation and genetic algorithm

This paper takes the vehicles scheduling of hazardous materials as the research object. First, considered the four objectives of risk minimization, cost minimization, risk equilibrium value minimization, and duration minimization, the vehicles robust scheduling of hazardous materials in multiple distribution centers with risk balance is constructed. Then, uncertain models are transformed into peer-to-peer models through the idea of robust discrete optimization. By introducing the crossover and mutation operators of genetic algorithm into particle swarm algorithm, a hybrid particle swarm algorithm is constructed, and three-stage coding rules are adopted. Finally, case study is exemplified to prove the feasibility of the model and algorithm. The results show that the obtained scheduling scheme for the vehicles of hazardous materials minimizes the risk value and makes the risk distribution more balanced for the government regulatory department, and the vehicle can avoid certain road sections with relatively large risk values. For transportation enterprises, the vehicle scheduling scheme for transporting hazardous materials minimizes the cost and shortens the task duration at the same time. In the case of risk uncertainty, different robust control parameters are introduced to make the vehicles robust scheduling of hazardous materials have different degrees of stability.

Session key based fast, secure and lightweight image encryption algorithm

Nowadays, most of the communications in IoT enabled devices are done in the form of images. To protect the images from intruders, there is a need for a secure encryption algorithm. Many encryption algorithms have been proposed, some of the algorithms are based on symmetric-key cryptography and others are based on asymmetric key cryptography. This work proposed a fast, secure, and lightweight symmetric image cryptographic algorithm based on the session key. In this work, for every image encryption, a new session key is generated. Here session keys are generated with the help of crossover and mutation operators of genetic algorithm. This proposed algorithm uses a 64-bit plain text and requires an 80-bit key, where 64-bits of a key is generated via symmetric hexadecimal key and the remaining 16-bits of a key are randomly added, to encrypt the image. Here crossover and mutation operators are used to generate random 64-bits of a key. The proposed algorithm will work for both color and grayscale images. The proposed algorithm is simulated on MATLAB 2017 platform and compared with similar types of the existing algorithm on various parameters.

Multi-point Mooring PID Control Based on Kalman Filtering-Genetic Algorithm-Slow Disturbance Observer Compensation

An adaptive PID control technique based on the Kalman Filtering (KF) Genetic Algorithm (GA) and Slow Disturbance Observer Compensation (SDOC) is proposed for multi-point mooring system in this paper. The multi-point mooring model, which is simplified to two-point mooring, is established, its self-tuning PID mooring controller based on KF-GA-SDOC is designed. The multi-point mooring model is updated on-line with the KF and SDOC to filter and observe time-varying signal; the PID mooring control gains are adjustable parameters by selection, crossover and mutation operator of GA; the mooring control system based on KF-GA-SDOC PID is optimized. The simulation results show that the designed KF-GA-SDOC PID mooring controller can maintain good control performance under different conditions.

Hybrid Artificial Chemical Reaction Optimization Algorithm for Cluster Analysis

Heuristic algorithms have significant contribution in the clustering field. In present work, a hybrid version of the artificial chemical reaction optimization algorithm (HACRO) is proposed to optimize clustering problems. As exploration and exploitation are two major aspects that require balanced coordination among algorithmic steps. The artificial chemical reaction suffers from slower convergence speed due to its poor exploitation mechanism. Moreover, it requires more execution time. Henceforth, to enhance the convergence speed and to make balance among algorithmic space a hybrid version of ACRO is developed. In present work, the artificial chemical reaction optimization algorithm is incorporated with crossover and mutation operator of genetic algorithm. Further, the efficiency of the HACRO algorithm is examined on seven benchmark datasets and collated with ACO, PSO, K-means, GA, ICSO and ACRO clustering algorithms. The present investigation indicated that the proposed algorithm works efficiently in clustering field.

An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem

Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints : production, worker, and inventory.Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the algorithm combining Particle Swarm Optimization (PSO) and mutation operator of Genetic Algorithm (GA) to improve optimal solution resulted by PSO. Three simulations using three different mutation probabilies : 0, 0.01 and 0.7 are applied to PSOGA. Futhermore, some mutation probabilities in PSOGA will be simulated and percent of improvement will be computed.Results: From the simulations, PSOGA can improve optimal solution of PSO and the position of improvement is also determined by mutation probability. The small mutation probability gives smaller chance to the particle to explore and form new solution so that the position of improvement of small mutation probability is in middle of iteration. The large mutation probability gives larger chance to the particle to explore and form new solution so that the position of improvement of large mutation probability is in early of iteration.Conclusion: Overall, the simulations show that PSOGA can improve optimal solution resulted by PSO and therefore it can give optimal cost spent by the company for the planning.Keywords: Constrained Optimization, Genetic Algorithm, Linear Programming, Particle Swarm Optimization, Production Planning

Long Link Wireless Sensor Routing Optimization Based on Improved Adaptive Ant Colony Algorithm

Compared with the advantages and disadvantages of genetic algorithm, based on the ant colony algorithm, this paper combined with the selection, crossover and mutation operation of genetic algorithm, the search speed and optimization ability of ant colony algorithm are improved. The optimal path evaluation function considers nodes. The energy consumption and the residual energy of the node enable the nodes with more residual energy to participate in the data forwarding preferentially and balance the energy consumption between the nodes. The comparison with the classical ant colony algorithm and the genetic algorithm shows that as the number of data forwarding rounds increases, the improved The ant colony algorithm has low energy consumption, many residual energy, and the network life cycle is obviously prolonged. With the increase of the network running time, the improved ant colony algorithm, the node equalization energy consumption is good, and the success rate of the optimal path search is also significantly better than the other two algorithms.

Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization

SummaryMobile edge computing (MEC) necessitates cost‐effective deployment for executing scientific workflows with different tasks and datasets, which provides computing, storage and network control at the network edge. However, the execution of scientific workflows in MEC results in heavy costs of data placement including data transmission and data storage. Although there are solutions for data placement in traditional cloud computing, they cannot effectively respond to the latency‐sensitive property of scientific workflows, which leads to the excessive costs of data placement. To cope with this problem, we combine the advantages of MEC and cloud computing and propose a genetic algorithm particle swarm optimization (GAPSO) based method to explore the optimal strategy of data placement for scientific workflows in MEC. First, a unified model of data placement is designed to explore a cost‐effective strategy, which considers the different characteristics between MEC and cloud computing as well as the impact of latency constraint on transmission costs. Next, the advantages of genetic algorithm (GA) and particle swarm optimization (PSO) are integrated to optimize the proposed model, which utilities the fast convergence of PSO and the crossover and mutation operations of GA. Simulations using real‐world scientific workflows show the effectiveness of the proposed method for reducing data placement costs in MEC.

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.