Perturbation operator analysis on ILS-RVND algorithm to solve CVRP

2020 ◽  
Author(s):  
M. Alwi Rozaq Ngisomuddin ◽  
Darmawan Satyananda
Keyword(s):  
Perturbation Operator

scholarly journals An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 225
Author(s):  
José García ◽  
Gino Astorga ◽  
Víctor Yepes
Keyword(s):  
Transfer Functions ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Set Covering ◽  
Covering Problem ◽  
Perturbation Operator ◽  
K Nearest Neighbors ◽  
Combinatorial Optimization Problems ◽  
Box Plots ◽  
Metaheuristic Techniques

The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.

scholarly journals On a heat problem involving the perturbed Hardy–Sobolev operator

2004 ◽  
Vol 134 (4) ◽  
pp. 683-693 ◽  
Author(s):  
Nirmalendu Chaudhuri ◽  
Kunnath Sandeep
Keyword(s):  
Weight Function ◽  
Bounded Domain ◽  
Perturbation Operator ◽  
Positive Weight ◽  
Border Line ◽  
Heat Problem

Let Ω be a bounded domain in Rn, n ≥ 3 and 0 ∈ Ω. It is known that the heat problem ∂u/∂t + Lλ*u = 0 in Ω × (0, ∞), u(x, 0) = u0 ≥ 0, u0 ≢ 0, where Lλ* := −Δ − λ*/|x|2, λ* := ¼(n − 2)2, does not admit any solutions for any t > 0. In this paper we consider the perturbation operator Lλ*q := −Δ − λ*q(x)/|x|2 for some suitable bounded positive weight function q and determine the border line between the existence and non-existence of positive solutions for the above heat problem with the operator Lλ*q. In dimension n = 2, we have similar phenomena for the critical Hardy–Sobolev operator L* := −Δ − (1/4|x|2)(log R/|x|)−2 for sufficiently large R.

Effective Algorithms for Scheduling Workflow Tasks on Mobile Clouds

2020 ◽  
Vol 29 (16) ◽  
pp. 2050255
Author(s):  
Heng Li ◽  
Yaoqin Zhu ◽  
Meng Zhou ◽  
Yun Dong
Keyword(s):  
Mobile Devices ◽  
Solution Space ◽  
Iterated Local Search ◽  
Mobile Cloud ◽  
Local Optimum ◽  
Perturbation Operator ◽  
Evolutionary Mechanisms ◽  
Local Optima ◽  
Np Hard Problem ◽  
Workflow Tasks

In mobile cloud computing, the computing resources of mobile devices can be integrated to execute complicated applications, in order to tackle the problem of insufficient resources of mobile devices. Such applications are, in general, characterized as workflows. Scheduling workflow tasks on a mobile cloud system consisting of heterogeneous mobile devices is a NP-hard problem. In this paper, intelligent algorithms, e.g., particle swarm optimization (PSO) and simulated annealing (SA), are widely used to solve this problem. However, both PSO and SA suffer from the limitation of easily being trapped into local optima. Since these methods rely on their evolutionary mechanisms to explore new solutions in solution space, the search procedure converges once getting stuck in local optima. To address this limitation, in this paper, we propose two effective metaheuristic algorithms that incorporate the iterated local search (ILS) strategy into PSO and SA algorithms, respectively. In case that the intelligent algorithm converges to a local optimum, the proposed algorithms use a perturbation operator to explore new solutions and use the newly explored solutions to start a new round of evolution in the solution space. This procedure is iterated until no better solutions can be explored. Experimental results show that by incorporating the ILS strategy, our proposed algorithms outperform PSO and SA in reducing workflow makespans. In addition, the perturbation operator is beneficial for improving the effectiveness of scheduling algorithms in exploring high-quality scheduling solutions.

A New Derivation of the Perturbation Operator Used in MCNP

2007 ◽  
Vol 4 (5) ◽  
pp. 100340 ◽  
Author(s):  
Robert E. Keith ◽  
D. W. Vehar ◽  
D. M. Gilliam ◽  
J. M. Adams ◽  
S. W. Dean
Keyword(s):  
Perturbation Operator

scholarly journals An Enhanced Two-Level Metaheuristic Algorithm with Adaptive Hybrid Neighborhood Structures for the Job-Shop Scheduling Problem

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Pisut Pongchairerks
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Metaheuristic Algorithm ◽  
Local Search Algorithm ◽  
Perturbation Operator ◽  
Job Shop Scheduling Problem ◽  
Neighborhood Structure ◽  
Shop Scheduling ◽  
Upper Level

For solving the job-shop scheduling problem (JSP), this paper proposes a novel two-level metaheuristic algorithm, where its upper-level algorithm controls the input parameters of its lower-level algorithm. The lower-level algorithm is a local search algorithm searching for an optimal JSP solution within a hybrid neighborhood structure. To generate each neighbor solution, the lower-level algorithm randomly uses one of two neighbor operators by a given probability. The upper-level algorithm is a population-based search algorithm developed for controlling the five input parameters of the lower-level algorithm, i.e., a perturbation operator, a scheduling direction, an ordered pair of two neighbor operators, a probability of selecting a neighbor operator, and a start solution-representing permutation. Many operators are proposed in this paper as options for the perturbation and neighbor operators. Under the control of the upper-level algorithm, the lower-level algorithm can be evolved in its input-parameter values and neighborhood structure. Moreover, with the perturbation operator and the start solution-representing permutation controlled, the two-level metaheuristic algorithm performs like a multistart iterated local search algorithm. The experiment’s results indicated that the two-level metaheuristic algorithm outperformed its previous variant and the two other high-performing algorithms in terms of solution quality.

A Perturbation-Operator Approach to Generalized Zeeman Effects

1969 ◽  
Vol 37 (9) ◽  
pp. 921-923 ◽  
Author(s):  
A. M. Arthurs ◽  
P. D. Robinson
Keyword(s):  
Perturbation Operator ◽  
Operator Approach

scholarly journals Approximating solutions of generalized pseudocontractive variational inequalities by admissible perturbation type iterative methods

2013 ◽  
Vol 22 (2) ◽  
pp. 237-241
Author(s):  
CRISTINA TICALA ◽  
Keyword(s):  
Variational Inequalities ◽  
Iterative Methods ◽  
Hilbert Spaces ◽  
Convex Sets ◽  
Perturbation Operator ◽  
Strongly Nonlinear ◽  
Admissible Perturbation

In this paper we give the solvability class of generalized strongly nonlinear variational inequalities modified by the use of the new concept of admissible perturbation operator on nonempty closed convex sets in Hilbert spaces.

Materialized View Selection Using Self-Adaptive Perturbation Operator-Based Particle Swarm Optimization

2020 ◽  
Vol 11 (3) ◽  
pp. 50-67
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Response Time ◽  
Data Warehouse ◽  
Particle Swarm ◽  
Data Warehouses ◽  
Perturbation Operator ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
View Selection ◽  
Self Adaptive

A data warehouse is a central repository of time-variant and non-volatile data integrated from disparate data sources with the purpose of transforming data to information to support data analysis. Decision support applications access data warehouses to derive information using online analytical processing. The response time of analytical queries against speedily growing size of the data warehouse is substantially large. View materialization is an effective approach to decrease the response time for analytical queries and expedite the decision-making process in relational implementations of data warehouses. Selecting a suitable subset of views that deceases the response time of analytical queries and also fit within available storage space for materialization is a crucial research concern in the context of a data warehouse design. This problem, referred to as view selection, is shown to be NP-Hard. Swarm intelligence have been widely and successfully used to solve such problems. In this paper, a discrete variant of particle swarm optimization algorithm, i.e. self-adaptive perturbation operator based particle swarm optimization (SPOPSO), has been adapted to solve the view selection problem. Accordingly, SPOPSO-based view selection algorithm (SPOPSOVSA) is proposed. SPOPSOVSA selects the Top-K views in a multidimensional lattice framework. Further, the proposed algorithm is shown to perform better than the view selection algorithm HRUA.

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