scholarly journals The New Approaches Based on Ant Colony Optimization and Greedy Search Algorithm for Solving Hierarchical Chinese Postman Problem with Stochastic Travel Times

2022 ◽  
Author(s):  
Özlem Çomaklı Sökmen ◽  
mustafa yılmaz
Keyword(s):  
Ant Colony Optimization ◽  
Heuristic Method ◽  
Real Life ◽  
Np Hard ◽  
Travel Times ◽  
Greedy Search ◽  
Chinese Postman Problem ◽  
Life Problems ◽  
Chinese Postman ◽  
Stochastic Travel Times

Abstract Hierarchical Chinese postman problem (HCPP), a variant of the Chinese postman problem, aims to find the shortest tour or tours by passing through the arcs classified according to precedence relationship. HCPP, which has a wide application area in real-life problems such as shovel snow and routing patrol vehicles where precedence relations are important, belongs to the NP-hard problem class. In real-life problems, travel time between the two locations in city traffic varies due to reasons such as traffic jam, weather conditions, etc. Therefore travel times are uncertain. In this study, HCPP is handled with the chance-constrained stochastic programming approach, and a new type of problem, hierarchical Chinese postman problem with stochastic travel times, is introduced. Due to the NP-hard nature of the problem, the developed mathematical model with stochastic parameter values cannot find proper solutions in large size problems within the appropriate time interval. Therefore, two new solution approaches, a heuristic method based on the Greedy Search (GSA) algorithm and a meta-heuristic method based on ant colony optimization (ACO) are proposed in this study. These new algorithms were tested on modified benchmark instances and randomly generated problem instances with as many as 817 edges. The performance of algorithms was compared in terms of solution quality and computational time.

Bio-Inspired Metaheuristics

2020 ◽  
Vol 10 (4) ◽  
pp. 1-18
Author(s):  
Rachid Kaleche ◽  
Zakaria Bendaoud ◽  
Karim Bouamrane
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Np Hard ◽  
Exact Methods ◽  
Approximate Methods ◽  
Life Problems ◽  
Comprehensive Survey ◽  
Hard Problems ◽  
Exact And Approximate Methods ◽  
Np Hard Problems

In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to implement and provide interesting results. This paper aims to provide a comprehensive survey of bio-inspired metaheuristics, their classification, principals, algorithms, their application domains, and a comparison between them.

scholarly journals The Chinese deliveryman problem

4OR ◽  
2019 ◽  
Vol 18 (3) ◽  
pp. 341-356
Author(s):  
Martijn van Ee ◽  
René Sitters
Keyword(s):  
Planar Graphs ◽  
Time Of Arrival ◽  
Np Hard ◽  
Chinese Postman Problem ◽  
Undirected Graphs ◽  
Connected Graphs ◽  
Depth First Search ◽  
Chinese Postman

Abstract We introduce the Chinese deliveryman problem where the goal of the deliveryman is to visit every house in his neighborhood such that the average time of arrival is minimized. We show that, in contrast to the well-known Chinese postman problem, the Chinese deliveryman problem is APX-hard in general and NP-hard for planar graphs. We give a simple $$\sqrt{2}$$ 2 -approximation for undirected graphs and a 4 / 3-approximation for 2-edge connected graphs. We observe that there is a PTAS for planar graphs and that depth first search is optimal for trees.

scholarly journals Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

2021 ◽  
Author(s):  
Saman Almufti
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Population Based ◽  
Performance Evaluations ◽  
Np Hard ◽  
Life Problems ◽  
Hard Problems ◽  
Optimum Solution ◽  
Over Time ◽  
Np Hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.

The Psychologist in the Medical School: An Example of Solving Real-Life Problems

1970 ◽  
Author(s):  
Matisyohu Weisenberg ◽  
Carl Eisdorfer ◽  
C. Richard Fletcher ◽  
Murray Wexler
Keyword(s):  
Medical School ◽  
Real Life ◽  
Life Problems

scholarly journals AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

scholarly journals Formulating Modes of Cooperative Leaning for Education for Sustainable Development

2021 ◽  
Vol 13 (6) ◽  
pp. 3465
Author(s):  
Jordi Colomer ◽  
Dolors Cañabate ◽  
Brigita Stanikūnienė ◽  
Remigijus Bubnys
Keyword(s):  
Sustainable Development ◽  
Real Life ◽  
Education For Sustainable Development ◽  
Professional Life ◽  
Global Challenges ◽  
Life Problems ◽  
The Face ◽  
Contemporary Education ◽  
The Impact

In the face of today’s global challenges, the practice and theory of contemporary education inevitably focuses on developing the competences that help individuals to find meaningfulness in their societal and professional life, to understand the impact of local actions on global processes and to enable them to solve real-life problems [...]

scholarly journals Derivative-Free King’s Scheme for Multiple Zeros of Nonlinear Functions

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1242
Author(s):  
Ramandeep Behl ◽  
Sonia Bhalla ◽  
Eulalia Martínez ◽  
Majed Aali Alsulami
Keyword(s):  
Iterative Methods ◽  
Fourth Order ◽  
Order Of Convergence ◽  
Real Life ◽  
Multiple Roots ◽  
Computational Results ◽  
Nonlinear Functions ◽  
First Order ◽  
Life Problems ◽  
Derivative Free

There is no doubt that the fourth-order King’s family is one of the important ones among its counterparts. However, it has two major problems: the first one is the calculation of the first-order derivative; secondly, it has a linear order of convergence in the case of multiple roots. In order to improve these complications, we suggested a new King’s family of iterative methods. The main features of our scheme are the optimal convergence order, being free from derivatives, and working for multiple roots (m≥2). In addition, we proposed a main theorem that illustrated the fourth order of convergence. It also satisfied the optimal Kung–Traub conjecture of iterative methods without memory. We compared our scheme with the latest iterative methods of the same order of convergence on several real-life problems. In accordance with the computational results, we concluded that our method showed superior behavior compared to the existing methods.

scholarly journals ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1456
Author(s):  
Stefka Fidanova ◽  
Krassimir Todorov Atanassov
Keyword(s):  
Decision Making ◽  
Knapsack Problem ◽  
Propositional Logic ◽  
Optimization Problems ◽  
Real Life ◽  
Combinatorial Optimization Problems ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Multiple Constraint

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

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