Non-uniform Mutation Operator Research Articles

Real-time sensor networks based on genetic algorithms application in the analysis of innovative data in cultural industry management

Against the backdrop of sustained economic development and steady improvement of China's overall national strength, the cultural industry is in a good stage of development, but it also faces problems such as data complexity and difficulty in organizing. Based on this, we propose a management innovation data analysis system based on genetic algorithm and real-time technology of the Internet of Things, and apply this method to cultural industry management to analyze relevant management data. It was found that based on the improved genetic algorithm, it can achieve better optimization results with less computational complexity. Meanwhile, genetic algorithms based on penalty functions adopt a non-uniform mutation operator, which can accelerate convergence. Compared with existing genetic algorithms, the new genetic algorithm has lower computational complexity and lower system average overhead, greatly improving work efficiency and making its work cost lower. On this basis, combined with theoretical and empirical research results, provide theoretical and practical support for the sustainable development of the industry.

A Robust Fractional-Order PID Controller Based Load Frequency Control Using Modified Hunger Games Search Optimizer

In this article, a recent modified meta-heuristic optimizer named the modified hunger games search optimizer (MHGS) is developed to determine the optimal parameters of a fractional-order proportional integral derivative (FOPID) based load frequency controller (LFC). As an interconnected system’s operation requires maintaining the tie-line power and frequency at their described values without permitting deviations in them, an enhanced optimizer is developed to identify the controllers’ parameters efficiently and rapidly. Therefore, the non-uniform mutation operator is proposed to strengthen the diversity of the solutions and discover the search landscape of the basic hunger games search optimizer (HGS), aiming to provide a reliable approach. The considered fitness function is the integral time absolute error (ITAE) comprising the deviations in tie-line power and frequencies. The analysis is implemented in two networks: the 1st network comprises a photovoltaic (PV) plant connected to the thermal plant, and the 2nd network has four connected plants, which are PV, wind turbine (WT), and 2 thermal units with generation rate constraints and governor dead-band. Two different load disturbances are imposed for two studied systems: static and dynamic. The results of the proposed approach of MHGS are compared with the marine predators algorithm (MPA), artificial ecosystem based optimization (AEO), equilibrium optimizer (EO), and Runge–Kutta based optimizer (RUN), as well as movable damped wave algorithm (DMV) results. Moreover, the performance specifications of the time responses of frequencies and tie-line powers’ violations comprising rise time, settling time, minimum/maximum setting values, overshoot, undershoot, and the peak level besides its duration are calculated. The proposed MHGS shows its reliability in providing the most efficient values for the FOPID controllers’ parameters that achieve the lowest fitness of 0.89726 in a rapid decaying. Moreover, the MHGS based system becomes stable the most quickly as it has the shortest settling time and is well constructed as it has the smallest peak, overshoots at early times, and then the system becomes steady. The results confirmed the competence of the proposed MHGS in designing efficient FOPID-LFC controllers that guarantee reliable operation in case of load disturbances.

Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Extracting the optimum parameters of solar photovoltaic (PV) model using the experimental data of current–voltage is very critical in simulating, controlling, and optimizing the PV systems. One of the important problems encountered in modeling and simulating is to find a model that can extract parameters from PV models quickly, accurately, and reliably. Based on this motivation, the goal of this study is to suggest an improved algorithm, namely genetic algorithm based on non-uniform mutation (GAMNU), in order to approximate efficiently the parameters of solar cells and PV modules. In GAMNU, non-uniform mutation operator is used to maintain diversity in the explored solutions and the crossover operator follows an adaptive search strategy, which consists of searching the entire space at the beginning while maintaining a focused search when the population tends to converge in a certain region of the search space. The performance of the method is comprehensively evaluated on different solar cell models, including single and double diode, and single diode PV modules, of a R.T.C France silicon solar cell, ESP-160 PPW PV, STP6-120/36 and Photowatt-PWP201 module. The results obtained from single and double diode models for R.T.C France are respectively 9.8618×10-4 and 9.8683×10-4, and for Photowatt-PWP201, STP6-120/36 and ESP-160 PPW are 2.3824×10-3 2.382420230900×10-3, 1.6735×10-21.6735786505085×10-2 and 8.2942×10-2 8.2942×10-2. The statistical obtained results show that the proposed method has very competitive performance in terms of accuracy and reliability when compared to other advanced algorithms. Therefore, the proposed algorithm is highly useful to extract the parameters of solar PV models.

Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators

In this paper, a heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (HCLDMS-PSO) is presented. In addition, a comprehensive learning (CL) strategy with the global optimal experience of the whole population is conducted to generate an exploitation subpopulation exemplar. However, a modified dynamic multi-swarm (DMS) strategy is specially designed to construct the exploration subpopulation exemplar. In the canonical DMS strategy, it is unfavorable for different sub-swarms to use the same linear decreasing inertia weight parameter. We first propose classifying the DMS sub-swarms at the search level and then constructing a novel nonlinear adaptive decreasing inertia weight for different sub-swarms, introducing a non-uniform mutation operator to enhance its exploration capability. Finally, the gbest of the whole population also adopts a Gaussian mutation operator to avoid falling into the local optimum. The particles of the two subpopulations will update their velocity independently without crippling one another to prevent a loss of diversity. The performance of HCLDMS-PSO is compared with those of 8 other PSO variants and 11 evolutionary algorithms on two classical benchmark optimization problems and a real-world engineering problem. Experimental results demonstrate that the HCLDMS-PSO improves the convergence speed, accuracy, and reliability on most optimization problems.

Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch

This paper proposes a novel multi-agent bargaining learning (MABL) for the distributed energy hub economic dispatch (EHED) of multiple energy carrier systems (MECS). Distributed EHED is developed by extending the conventional economic dispatch (ED) into MECS in a distributed manner, in which each energy hub is regarded as a learning agent for self-scheduling. The classical Q-learning with associative memory is employed for knowledge learning of each agent, while the non-uniform mutation operator is adopted for handling the continuous control variables. To maximize the total payoff of all the energy hubs, the bargaining game is presented for achieving an effective coordination between the buyer agents and a seller agent, where the slack energy hub is designed as the seller agent and the others are the buyer agents. MABL has been thoroughly evaluated for the distributed EHED on a high-complex 39-hub MECS with 29 energy hub structures and 76 energy production units. Case studies verify the superior performance of MABL for the distributed EHED compared with six centralized heuristic optimization algorithms.

Globally-optimal prediction-based adaptive mutation particle swarm optimization

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm.

Co‐optimisation for distribution networks with multi‐microgrids based on a two‐stage optimisation model with dynamic electricity pricing

Microgrids (MG) with renewable sources and storage devices play an important role in distribution networks (DNs). MG economic load dispatch normally does not support DN, especially when they belong to different companies and share different interests. To achieve optimal operation of DN with multi-MGs, a two-stage optimisation model is established based on the dynamic electricity pricing strategy. The dynamic electricity pricing strategy gives incentive for MG to participate in the operation of DN, and it is determined by the matching degree between exchange power expected by DN and the real exchange power. Moreover, the life time characteristic of a storage device system is taken into consideration in the second stage via the rain-flow-counting method. The multi-objective particle swarm optimisation is applied to solve the two-stage optimisation problem. Besides, initial particles generated based on Latin hypercube sampling, global best position obtained based on adaptive selection, and non-uniform mutation operator are applied to improve search efficiency as well as maintain the population diversity of the algorithm. Simulation results show that the power sharing between MGs’ and DNs’ can smooth the load curve of DN as well as reduce the total operation cost of MGs.

Cloud computing resource scheduling and leasing algorithm based on extreme price filter

A cloud computing virtual resource leasing algorithm considering about extreme price filtering has been proposed in this paper, which realises optimal selection for handling leasing price for task virtual machine. First, three-function module cloud composed of virtual resource provider, cloud service provider and final user has been adopted to calculate environment and list calculation target for virtual resource leasing profit; second, distribution and task urgency of price has been fully considered. For weakly stationary price sequence, outlier detection method has been adopted to make extreme price filtering. At the same time, weak equilibrium operator has been designed, exponential function has been used to control the overall shape of curve, non-uniform mutation operator has been used to make local operating adjusting, realise effective prediction for price in the future and optimal selection and make optimal selection for handling leasing price of task virtual machine.

Cloud Computing Task Scheduling Based on Cultural Genetic Algorithm

The task scheduling strategy based on cultural genetic algorithm(CGA) is proposed in order to improve the efficiency of task scheduling in the cloud computing platform, which targets at minimizing the total time and cost of task scheduling. The improved genetic algorithm is used to construct the main population space and knowledge space under cultural framework which get independent parallel evolution, forming a mechanism of mutual promotion to dispatch the cloud task. Simultaneously, in order to prevent the defects of the genetic algorithm which is easy to fall into local optimum, the non-uniform mutation operator is introduced to improve the search performance of the algorithm. The experimental results show that CGA reduces the total time and lowers the cost of the scheduling, which is an effective algorithm for the cloud task scheduling.

An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems

The multi-objective economic dispatch (MOED) problem in cascaded hydropower systems is a complicated nonlinear optimization problem with a group of complex constraints. In this paper, an improved partheno genetic algorithm (IPGA) for resolving the MOED problem in hydropower energy systems based on the non-uniform mutation operator is proposed. In the new algorithm, the crossover operator is removed and only mutation operation is made, which makes it simpler than GA in the genetic operations and not generate invalid offspring during evolution. With the help of incorporating greedy selection idea into the non-uniform mutation operator, IPGA searches the solution space uniformly at the early stage and very locally at the later stage, which makes it avoid the random blind jumping and stay at the promising solution areas. Finally, the proposed algorithm is applied to a realistic hydropower energy system with two giant scale cascaded hydropower plants in China. Compared with other algorithms, the results obtained using IPGA verify its superiority in both efficiency and precision.

Development and Demonstration of an Artificial Immune Algorithm for Mangrove Mapping Using Landsat TM

Mangroves are valuable contributors to coastal ecosystems; knowledge of the dynamics of mangrove ecosystems is important in the context of global change. To obtain this knowledge, remote sensing is an indispensable means, yet it poses challenges since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this letter, we proposed a modified artificial immune algorithm (AIA), in which the antibodies represent the candidate solutions and the antigens are expressed by the fitness function. Multiclass coevolution was combined with the concept of clonal selection to ensure computation of an optimal clustering center in parallel for each land cover type. A cluster-center-oriented decimal encoding method for antibodies was adopted, and the inner class variance and the between-class difference together were used to formulate the fitness function. Furthermore, a design of the antibody solubility-based selection operator and nonuniform mutation operator was undertaken. Applying this modified AIA to a Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in southeastern China, we found that the AIA substantially improved classification accuracy over traditional methods, showing an overall accuracy of 90% (kappa coefficient = 0.88) and was capable to discern mangrove well (commission of 10% and omission of 22%).

Multi-objective differential evolution algorithm based on the non-uniform mutation

A multi-objective differential evolution algorithm based on the non-uniform MDEM is proposed. MDEM employs the (μ + λ) selection strategy and non-uniform mutation operator to generate new population. The non-uniform mutation operator not only increased the pressure of the choice of MDEM algorithm, but also maintained the population diversity. We compare MDEM with NSGA-II, SPEA2 and DEMO by the numerical experiments on eight benchmark problems to find that MDEM has better computational results and effectiveness.

A Modified Non-Dominated Sorting Genetic Algorithm with Fractional Factorial Design for Multi-Objective Optimization Problems

AbstractThis study develops an intelligent non-dominated sorting genetic algorithm (GA), called INSGA herein, which includes a non-dominated sorting, crowded distance sorting, binary tournament selection, intelligent crossover and non-uniform mutation operators, for solving multi-objective optimization problems (MOOPs). This work adopts Goldberg's notion of non-dominated sorting and Deb's crowded distance sorting in the proposed MOGA to achieve solutions with good diversity-preservation and uniform spread on the approximated Pareto front. In addition, the chromosomes of offspring are generated based on an intelligent crossover operator using a fractional factorial design to select good genes from parents intelligently and achieve the goals of fast convergence and high numerical accuracy. To further improve the fine turning capabilities of the presented MOGA, a non-uniform mutation operator is also applied. A typical mutation approach is to create a random number and then add it to corresponding original value. Performance evaluation of the INSGA is examined by applying it to a variety of unconstrained and constrained multi-objective optimization functions. Moreover, two engineering design problems, which include a two-bar truss design and a welded beam design, are studied by the proposed INSGA. Results include the estimated Pareto-optimal front of non-dominated solutions.

Convergence Analysis of Evolutionary Algorithm Based on Non-Uniform Mutation for Multi-Dimensional and Multimodal Functions

The empirical and mechanism analysis based on evolutionary algorithm(EA) for non-uniform mutation operator show its excellent performance.Recently the convergence analysis of EA with non-uniform mutation operator is provided for one-dimensional functions. In this paper,based on the Markov process theory the convergence is generalized to general multi-dimensional and multimodal functions.The performance comparison with FEP indicates that the proposed EA model has good performance and potential application.

Improved particle filtering techniques based on generalized interactive genetic algorithm

This paper improves the resampling step of particle filtering (PF) based on a broad interactive genetic algorithm to resolve particle degeneration and particle shortage. For target tracking in image processing, this paper uses the information coming from the particles of the previous fame image and new observation data to self-adaptively determine the selecting range of particles in current fame image. The improved selecting operator with jam gene is used to ensure the diversity of particles in mathematics, and the absolute arithmetical crossing operator whose feasible solution space being close about crossing operation, and non-uniform mutation operator is used to capture all kinds of mutation in this paper. The result of simulating experiment shows that the algorithm of this paper has better iterative estimating capability than extended Kalman filtering (EKF), PF, regularized partide filtering (RPF), and genetic algorithm (GA)-PF.

A New Concept of Power Flow Analysis

The solution of the power flow is one of the most important problems in electrical power systems. These traditional methods such as Gauss-Seidel method and Newton-Raphson (NR) method have had drawbacks up to now such as initial values, abnormal operating solutions and divergences in heavy loads. In order to overcome theses problems, the power flow solution incorporating genetic algorithm (GA) is introduced in this paper. General operator of genetic algorithm, arithmetic crossover, and non-uniform mutation operator of GA are suggested to solve the power flow problem. While abnormal solution cannot be obtained by a NR method, multiple power flow solution can be obtained by a GA method. With a heavy load, both normal solution and abnormal solution can be obtained by a proposed method. In this paper, a floating number representation instead of the binary number representation is introduced for accuracy. Simulation results have been compared with traditional methods.