scholarly journals Computing strong metric dimension of some special classes of graphs by genetic algorithms

2008 ◽  
Vol 18 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Jozef Kratica ◽  
Vera Kovacevic-Vujcic ◽  
Mirjana Cangalovic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graph Coloring ◽  
Crew Scheduling ◽  
Metric Dimension ◽  
Hard Problem ◽  
Genetic Operators ◽  
Strong Metric Dimension ◽  
Np Hard Problem ◽  
Special Classes

In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.

scholarly journals Bounds of Fractional Metric Dimension and Applications with Grid-Related Networks

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1383
Author(s):  
Ali H. Alkhaldi ◽  
Muhammad Kamran Aslam ◽  
Muhammad Javaid ◽  
Abdulaziz Mohammed Alanazi
Keyword(s):  
Metric Dimension ◽  
Hard Problem ◽  
Np Hard ◽  
Weighted Version ◽  
Sharp Bounds ◽  
Np Hard Problem ◽  
Metric Dimensions

Metric dimension of networks is a distance based parameter that is used to rectify the distance related problems in robotics, navigation and chemical strata. The fractional metric dimension is the latest developed weighted version of metric dimension and a generalization of the concept of local fractional metric dimension. Computing the fractional metric dimension for all the connected networks is an NP-hard problem. In this note, we find the sharp bounds of the fractional metric dimensions of all the connected networks under certain conditions. Moreover, we have calculated the fractional metric dimension of grid-like networks, called triangular and polaroid grids, with the aid of the aforementioned criteria. Moreover, we analyse the bounded and unboundedness of the fractional metric dimensions of the aforesaid networks with the help of 2D as well as 3D plots.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

scholarly journals Metric dimension of metric transform and wreath product

2019 ◽  
Vol 11 (2) ◽  
pp. 418-421
Author(s):  
B.S. Ponomarchuk
Keyword(s):  
Wreath Product ◽  
Metric Space ◽  
Metric Spaces ◽  
Metric Dimension ◽  
Hard Problem ◽  
Np Hard ◽  
Resolving Set ◽  
Np Hard Problem ◽  
Metric Transform ◽  
Empty Subset

Let $(X,d)$ be a metric space. A non-empty subset $A$ of the set $X$ is called resolving set of the metric space $(X,d)$ if for two arbitrary not equal points $u,v$ from $X$ there exists an element $a$ from $A$, such that $d(u,a) \neq d(v,a)$. The smallest of cardinalities of resolving subsets of the set $X$ is called the metric dimension $md(X)$ of the metric space $(X,d)$. In general, finding the metric dimension is an NP-hard problem. In this paper, metric dimension for metric transform and wreath product of metric spaces are provided. It is shown that the metric dimension of an arbitrary metric space is equal to the metric dimension of its metric transform.

scholarly journals Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

2020 ◽  
Vol 9 (3) ◽  
pp. 350
Author(s):  
Muhammad Ezar Al Rivan ◽  
Bhagaskara Bhagaskara
Keyword(s):  
Genetic Algorithm ◽  
Learning Rate ◽  
Hard Problem ◽  
Np Hard ◽  
Global Learning ◽  
Mutation Process ◽  
Hard Constraint ◽  
Multi Objective ◽  
Several Variables ◽  
Np Hard Problem

The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.

scholarly journals SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Pengfei Wang ◽  
Ruiyun Yu
Keyword(s):  
Genetic Algorithm ◽  
Greedy Algorithm ◽  
Task Allocation ◽  
Large Scale ◽  
Hard Problem ◽  
Optimal Result ◽  
Real Dataset ◽  
Np Hard Problem ◽  
Allocation Algorithms ◽  
Real Vehicle

Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total saved distance) is an NP-hard problem. We propose a heuristic greedy algorithm called Saving Most First (SMF) which is simple and effective in assigning tasks. Then, an optimized SMF based genetic algorithm (SMF-GA) is devised to jump out of the local optimal result. Finally, we demonstrate the performance of SMF and SMF-GA with extensive evaluations, based on a large scale real vehicle traces. The evaluation with large scale real dataset indicates that both SMF and SMF-GA algorithms outperform other benchmark algorithms in terms of saved distance, participant profits, etc.

Deterministic Model and Genetic Algorithm of Joint Inventory Control for Virtual Logistics

2012 ◽  
Vol 472-475 ◽  
pp. 97-101
Author(s):  
Da Li Jiang ◽  
Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Control Problem ◽  
Inventory Control ◽  
Computational Experiment ◽  
Deterministic Model ◽  
Control Model ◽  
Hard Problem ◽  
Np Hard Problem ◽  
Inventory Control Model ◽  
Inventory Control Problem

Virtual logistics is the trend of development for modern logistics. The problem of optimum of whole inventory of enterprises inside the virtual logistics organization should be solved immediately. This paper makes a deep research on virtual logistics joint inventory control problem, and puts forward an inventory control model based on indirect grouping. The model is a NP-Hard problem, so we put forward a genetic algorithm to solve the problem. The results of computational experiment prove the performance of both the model and the genetic algorithm.

SIMULATION OF GENETIC ALGORITHMS ON MIMD MULTICOMPUTERS

1992 ◽  
Vol 02 (04) ◽  
pp. 381-389 ◽  
Author(s):  
I. DE FALCO ◽  
R. DEL BALIO ◽  
E. TARANTINO ◽  
R. VACCARO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Diffusion Processes ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Parallel Genetic Algorithm ◽  
Genetic Operators ◽  
Parallel Diffusion ◽  
Fully Connected ◽  
Chordal Ring

In this paper, a Parallel Genetic Algorithm has been developed and mapped onto a coarse grain MIMD multicomputer whose processors have been configured in a fully connected chordal ring topology. In this way, parallel diffusion processes of good local information among processors have been carried out. The Parallel Genetic Algorithm has been applied, specifically, to the Travelling Salesman Problem. Many experiments have been performed with different combinations of genetic operators; the test results suggest that PMX crossover can be avoided by using only the inversion genetic operator and that a diffusion process leads to improved search in Parallel Genetic Algorithms.

Solving forest harvesting problem with artificial intelligence approaches: An example in Taiwan

2021 ◽  
pp. 1-12
Author(s):  
Yi-Chih Hsieh ◽  
Peng-Sheng You ◽  
Hao-Chun Chuang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Practical Problem ◽  
Forest Harvesting ◽  
Immune Algorithm ◽  
Test Problems ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem

In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives.

scholarly journals GCLIQUE: An Open Source Genetic Algorithm for the Maximum Clique Problem

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Genetic Algorithm ◽  
Maximum Clique ◽  
Maximum Size ◽  
The Other ◽  
Maximum Clique Problem ◽  
Hard Problem ◽  
Maximum Cliques ◽  
Np Hard Problem ◽  
Optimum Solution ◽  
Clique Problem

<p>A clique in a graph is a set of vertices that are all connected to each</p><p>other. A maximum clique is a clique of maximum size. A graph may have</p><p>more than one maximum cliques. The problem of finding a maximum</p><p>clique is a strongly hard NP-hard problem. It is not possible to find an</p><p>approximation algorithm which finds a maximum clique that is a constant</p><p>factor of the optimum solution. In this work, we present a genetic algorithm</p><p>for the maximum clique problem that is able to find optimum or</p><p>close to optimum solutions to most DIMACS graphs. The genetic algorithm</p><p>uses new crossover mechanisms which are able to find reasonably</p><p>good cliques which can then be used in other applications downstream.</p><p>We also provide C++ code for our algorithm. Results show that our algorithm</p><p>is able to find maximum cliques for most DIMACS instances, and</p><p>if not, close to optimum solutions for the other instances.</p>

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