Fusing Binary Particle Swarm Optimzation with Simulated Annealing for Knapsack Problems

Anantathanavit, Mana; Munlin, Mud-Armeen

doi:10.1109/iciea.2014.6931496

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Te method of simulated annealing was developed simultaneously by Kirk in 1983 [33]. Tis method is based on the algorithm of metropolis [34]. Tis algorithm allows us to get out of local minima with a high probability if the temperature T is high and to keep the most probable states for very low temperatures.…”

Section: Simulated Annealing Methodmentioning

confidence: 99%

Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Abid

Kafhali

Amzil

et al. 2023

Mathematical Problems in Engineering

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Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases.

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Section: Simulated Annealing Methodmentioning

confidence: 99%

Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Abid

Kafhali

Amzil

et al. 2023

Mathematical Problems in Engineering

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“…In global optimization problem, it is often required to search the global optimum among many local optima. Like others population algorithms, the PSO algorithm also suffers for the premature convergence rate and traps into local optima when solving the multimodal problems [13]. When a particle in a swarm finds its best current local position, the other particles will gather close to it rapidly.…”

Section: Radius Particle Swarm Optimizationmentioning

confidence: 99%

Using K-means Radius Particle Swarm Optimization for the Travelling Salesman Problem

Anantathanavit

Munlin

2015

IETE Technical Review

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The travelling salesman problem (TSP) is a well-known established scheduling problem. We propose a novel method to solve the TSP using the divide-and-conquer strategy. We employ K-means to cluster the sub-cities and then solve a sequence of sub-cities in a given order and merge them by the radius particle swarm optimization (RPSO). The RPSO incorporates adaptive mutation to avoid the impact of the bound of the solution. In addition, a local search procedure is embedded into the RPSO to accelerate the convergence and improve the solution. The performance of our proposed method is tested on a number of instances from the travelling salesman problem library (TSPLIB).Computational results and comparisons have demonstrated the effectiveness of the method.

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“…Optimization attempts to find the best-match solution with the lowest error value in a multidimensional analysis space of possibilities [27]. Furthermore, simulated annealing (SA) is a Matlab toolbox method used to solve unconstrained and constrained optimization problems [31,32]. The models of this method simulate the heating process of the materials.…”

mentioning

confidence: 99%

Optimal Coordination of Wind Power and Pumped Hydro Energy Storage

et al. 2019

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A study combining wind power with pumped hydro energy storage for the Jordanian utility grid is presented. Three solvers of the Matlab optimization toolbox are used to find the optimal solution for the cost of energy in a combined on-grid system. Genetic algorithm, simulated annealing (SA), and pattern search (PS) solvers are used to find the optimal solution. The GA solution of 0.0955388 $/kWh is economically feasible. This is 28.7% lower than the electricity purchased from the conventional utility grid. The discounted payback period to recover the total cost is 10.271 years. The suggested configuration is shown to be feasible by comparing it to real measurements for this case and a previous wind-only case. It is shown that the indicators of the optimal solution are improved. For instance, carbon dioxide emissions (ECO2) and conventional grid energy purchases are reduced by 24.69% and 24.68%, respectively. Moreover, it is shown that the benefits of adding hydro storage, combined with increasing the number of wind turbine units, reduces the cost of energy of renewables (COERenewables). Therefore, combining hydro storage with wind power is economically, environmentally, and technically a more efficient alternative to the conventional power generation.

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Fusing Binary Particle Swarm Optimzation with Simulated Annealing for Knapsack Problems

Cited by 4 publications

References 16 publications

Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Using K-means Radius Particle Swarm Optimization for the Travelling Salesman Problem

Optimal Coordination of Wind Power and Pumped Hydro Energy Storage

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