scholarly journals LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Seyedeh Maedeh Mirmohseni ◽  
Amir Javadpour ◽  
Chunming Tang
Keyword(s):  
Load Balancing ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Reducing Power ◽  
Initial Population ◽  
Genetic Optimization ◽  
Cloud Network ◽  
Better Than ◽  
Genetic Optimization Algorithm

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.

Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 304-308
Author(s):  
Lu Gang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Extreme Points ◽  
Function Optimization ◽  
Experimental Result ◽  
Genetic Optimization ◽  
Binary Coding ◽  
Coding Method ◽  
Peak Function ◽  
Better Than

In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.

scholarly journals Task-Scheduling Based on Multi-Objective Particle Swarm Optimization in Spatial Crowdsourcing

2019 ◽  
Vol 8 (1) ◽  
pp. 45-57
Author(s):  
Afra A. Alabbadi and Maysoon F. Abulkhair Afra A. Alabbadi and Maysoon F. Abulkhair
Keyword(s):  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Physical World ◽  
Spatial Crowdsourcing ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Future Work

As a result of the rapid growth of internet and smartphone technology, a novel platform that attracts individuals and groups known as crowdsourcing emerged. Crowdsourcing is an outsourcing platform that facilitates the accomplishment of costly tasks that consume long periods of time when traditional methods are used. Spatial crowdsourcing (SC) is based on location; it introduces a new framework for the physical world that enables a crowd to complete spatialtemporal tasks. The primary issue in SC is the assignment and scheduling of a set of available tasks to a set of proper workers based on different factors, such as the location of the task, the distance between task location and hired worker location, temporal conditions, and incentive rewards. In the real-world, SC applications need to optimize multi-objectives simultaneously to exploit the utility of SC, and these objectives can be in conflict. However, there are few studies that address this multi-objective optimization problem within a SC environment. Thus, the authors propose a multi-objective task scheduling optimization problem in SC that aims to maximize the number of completed tasks, minimize total travel cost, and ensure worker workload balance. To solve this problem, we developed a method that adapts the multi-objective particle swarm optimization (MOPSO) algorithm based on a proposed novel fitness function. The experiments were conducted with both synthetic and real datasets; the experimental results show that this approach provides acceptable initial results. As future work, we plan to improve the effectiveness of our proposed algorithm by integrating a simple ranking strategy based on task entropy and expected travel costs to enhance MOPSO performance.

scholarly journals Efficient trajectory parameterization for environmental optimization of departure flight paths using a genetic algorithm

2016 ◽  
Vol 231 (6) ◽  
pp. 1115-1123 ◽  
Author(s):  
S Hartjes ◽  
HG Visser
Keyword(s):  
Optimization Problem ◽  
Problem Formulation ◽  
Genetic Optimization ◽  
Noise Abatement ◽  
Fuel Burn ◽  
Side Constraints ◽  
Multiparameter Optimization ◽  
Optimization Problem Formulation ◽  
Genetic Optimization Algorithm

In this study, a genetic optimization algorithm is applied to the design of environmentally friendly aircraft departure trajectories. The environmental optimization has been primarily focused on noise abatement and local NOx emissions, whilst taking fuel burn into account as an economical criterion. In support of this study, a novel parameterization approach has been conceived for discretizing the lateral and vertical flight profiles, which reduces the need to include nonlinear side constraints in the multiparameter optimization problem formulation, while still permitting to comply with the complex set of operational requirements pertaining to departure procedures. The resulting formulation avoids infeasible solutions and hence significantly reduces the number of model evaluations required in the genetic optimization process. The efficiency of the developed approach is demonstrated in a case study involving the design of a noise abatement departure procedure at Amsterdam Airport Schiphol in The Netherlands.

scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

2021 ◽  
pp. 095440622199748
Author(s):  
Youyu Liu ◽  
Xuyou Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
B Splines ◽  
Multi Objective ◽  
Searching Strategy ◽  
Better Than

In order to improve the quality of the non-inferior solutions obtained by multi-objective particle swarm optimization (MOPSO), an improved algorithm called external archives self-searching multi-objective particle swarm optimization (EASS-MOPSO) was proposed and applied to a multi-objective trajectory optimization problem for manipulators. The position curves of joints were constructed by using quartic B-splines; the mathematical models of time, energy and jerk optimization objectives for manipulators were established; and the kinematic constraints of joints were transformed into the constraints of the control vertexes of the B-splines. A self-searching strategy of external archives to make non-inferior solutions have the ability to search the surrounding hyperspace was explored, and a diversity maintaining strategy of the external archives was proposed. The results of several test functions by simulation show that the convergence and diversity of the proposed algorithm are better than those of other 4 selected algorithms; the results of the trajectory optimization problem for manipulators by simulation show that the convergence, diversity and time consumption of the proposed algorithm are significantly better than those of non-dominated sorting genetic algorithm.

scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

scholarly journals A Simple Fitness Function for Minimum Attribute Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Yuebin Su ◽  
Jin Guo ◽  
Zejun Li
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Fitness Function ◽  
Attribute Reduction ◽  
Optimal Solution ◽  
Boolean Space ◽  
Fitness Functions ◽  
Minimal Subset ◽  
Classification Quality ◽  
Better Than

The goal of minimal attribute reduction is to find the minimal subsetRof the condition attribute setCsuch thatRhas the same classification quality asC. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.

scholarly journals A Novel Method of Optimal Capacitor Placement in the Presence of Harmonics for Power Distribution Network Using NSGA-II Multi-Objective Genetic Optimization Algorithm

2020 ◽  
Vol 25 (1) ◽  
pp. 17
Author(s):  
Majid Ebrahimi Moghadam ◽  
Hamid Falaghi ◽  
Mahdi Farhadi
Keyword(s):  
Optimization Algorithm ◽  
Reactive Power ◽  
Reducing Power ◽  
Optimal Placement ◽  
Genetic Optimization ◽  
Nonlinear Loads ◽  
Nsga Ii ◽  
Multi Objective ◽  
Capacitor Banks ◽  
Genetic Optimization Algorithm

One of the effective ways of reducing power system losses is local compensation of part of the reactive power consumption by deploying shunt capacitor banks. Since the capacitor’s impedance is frequency-dependent and it is possible to generate resonances at harmonic frequencies, it is important to provide an efficient method for the placement of capacitor banks in the presence of nonlinear loads which are the main cause of harmonic generation. This paper proposes a solution for a multi-objective optimization problem to address the optimal placement of capacitor banks in the presence of nonlinear loads, and it establishes a reasonable reconciliation between costs, along with improvement of harmonic distortion and a voltage index. In this paper, while using the harmonic power flow method to calculate the electrical quantities of the grid in terms of harmonic effects, the non-dominated sorting genetic (NSGA)-II multi-objective genetic optimization algorithm was used to obtain a set of solutions named the Pareto front for the problem. To evaluate the effectiveness of the proposed method, the problem was tested for an IEEE 18-bus system. The results were compared with the methods used in eight other studies. The simulation results show the considerable efficiency and superiority of the proposed flexible method over other methods.

scholarly journals PID Parameters Optimization Research for Hydro Turbine Governor by an Improved Fuzzy Particle Swarm Optimization Algorithm

2016 ◽  
Vol 10 (1) ◽  
pp. 101-117 ◽  
Author(s):  
Chen Gonggui ◽  
Du Yangwei ◽  
Guo Yanyan ◽  
Huang Shanwai ◽  
Liu Lilan
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Control ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Parameters Optimization ◽  
Simulation Experiments ◽  
Swarm Optimization

Parameter optimization of water turbine regulating system (WTRS) is decisive in providing support for the power quality and stability analysis of power system. In this paper, an improved fuzzy particle swarm optimization (IFPSO) algorithm is proposed and used to solve the optimization problem for WTRS under frequency and load disturbances conditions. The novel algorithm which is based on the standard particle swarm optimization (PSO) algorithm can speed up the convergence speed and improve convergence precision with combination of the fuzzy control thought and the crossover thought in genetic algorithm (GA). The fuzzy control is employed to get better dynamics of balance between global and local search capabilities, and the crossover operator is introduced to enhance the diversity of particles. Two different types of WTRS systems are built and analyzed in the simulation experiments. Furthermore, the sum of regulating time and another number that is the integral of sum for absolute value of system error and the squared governor output signal is considered as the fitness function of this algorithm. The simulation experiments for parameter optimization problem of WTRS system are carried out to confirm the validity and superiority of the proposed IFPSO, as compared to standard PSO, Ziegler Nichols (ZN) algorithm and fuzzy PID algorithm in terms of parameter optimization accuracy and convergence speed. The simulation results reveal that IFPSO significantly improves the dynamic performance of system under all of the running conditions.

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