scholarly journals Analysis of the adaptive algorithms behaviour applied to the railway optimization problems

2017 ◽  
Vol 109 ◽  
pp. 560-567
Author(s):  
F.F. Pashchenko ◽  
A.F. Pashchenko ◽  
N.A. Kuznetsov ◽  
I.K. Minashina ◽  
E.M. Zakharova
Keyword(s):  
Optimization Problems ◽  
Adaptive Algorithms ◽  
Railway Optimization

Hybrid Bat Algorithm for Balancing Load in Cloud Computing

2018 ◽  
Vol 7 (4.12) ◽  
pp. 26 ◽  
Author(s):  
Shabnam Sharma ◽  
Dr. Sahil Verma ◽  
Dr. Kiran Jyoti ◽  
Dr. Kavita
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Minimum Spanning Tree ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Routing Optimization ◽  
Railway Optimization ◽  
Vehicle Routing Optimization ◽  
Planning Problems

Swarm Intelligence is proven to be beneficial for solving many problems including knapsack problem, minimum spanning tree, planning problems, routing, load balancing and many more. Here, the focus of the work is on bat algorithm. Due to astonishing feature of echolocation, bat algorithm has drawn researcher’s attention in past years. It is applicable in solving different problems such vehicle routing optimization, time-tabling in railway optimization problems, load balancing in cloud computing etc. The main objective of this work is to propose a technique balancing the load among virtual machines in cloud computing environment. 

scholarly journals Adaptive Strategies Based on Differential Evolutionary Algorithm for Many-Objective Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yifei Sun ◽  
Kun Bian ◽  
Zhuo Liu ◽  
Xin Sun ◽  
Ruoxia Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Amplification Factor ◽  
Optimization Problems ◽  
Adaptive Algorithms ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Crossover Probability ◽  
Multiobjective Optimization Problems ◽  
Differential Evolutionary Algorithm ◽  
Test Suites

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems.

scholarly journals An Adaptive Multi-Swarm Optimizer for Dynamic Optimization Problems

2014 ◽  
Vol 22 (4) ◽  
pp. 559-594 ◽  
Author(s):  
Changhe Li ◽  
Shengxiang Yang ◽  
Ming Yang
Keyword(s):  
Experimental Study ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Adaptive Algorithms ◽  
Dynamic Environments ◽  
Global Optimum ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Research Areas ◽  
Moving Peaks

The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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