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1

A hybrid algorithm based on non-dominated sorting ant colony and genetic algorithmsfor solving multi-objective multi-mode project scheduling problems under resource constraints

Bagherinejad Jafar, Jolai Fariborz, Abdollahnejad Raheleh, Shoeib Mahnaz

Management and Production Engineering Review

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2020

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Vol. 11, No. 2

88--98

EN

A project scheduling problem investigates a set of activities that have to be scheduled due to precedence priority and resource constraints in order to optimize project-related objective functions. This paper focuses on the multi-mode project scheduling problem concerning resource constraints (MRCPSP). Resource allocation and leveling, renewable and non-renewable resources, and time-cost trade-oﬀ are some essential characteristics which are considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid algorithm is proposed based on non-dominated sorting ant colony optimization and genetic algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to improve the eﬃciency of the ant colony algorithm. The test problems are generated based on the project scheduling problem library (PSPLIB) to compare the eﬃciency of the proposed algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result veriﬁes the eﬃciency of the proposed hybrid algorithm in comparison to the NSGA-II algorithm.

2

Ship route planning using historical trajectories derived from AIS Data

He Y. K., Zhang D., Zhang J. F., Zhang M. Y., Li T. W.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2019

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Vol. 13, no. 1

69--76

EN

Ship route planning is one of the key issues in enhancing traffic safety and efficiency. Many route planning methods have been developed, but most of them are based on the information from charts. This paper proposes a method to generate shipping routes based on historical ship tracks. The ship's historical route information was obtained by processing the AIS data. From which the ship turning point was extracted and clustered as nodes. The ant colony algorithm was used to generate the optimize route. The ship AIS data of the Three Gorges dam area was selected as a case study. The ships’ optimized route was generated, and further compared with the actual ship's navigation trajectory. The results indicate that there is space of improvement for some of the trajectories, especially near the turning areas.

3

Emergency Evacuation Route Choice Based on Improved Ant Colony Algorithm

Wang Bo, Guo Lin, Zhou Jibiao, Hu Danjuan, Pawęska Marcin, Šimák Ladislav, Ristvej Jozef, Jánošíková Michaela

Logistics and Transport

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2019

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Vol. 44, No. 4

33--39

EN

In order to explore the optimal route choice for emergency evacuation in the campus, we propose a novel route choice method based on brittle characteristics of campus system and improved ant colony algorithm. Both optimal and worst-case emergency evacuation routes are simulated in the campus of Ningbo University of Technology. From the simulation, the length of optimal and worse-case evacuation routes between the starting point and eight exits can be obtained by adjusting the importance value of trip distance and the degree of conformity, under the condition of static relative importance of pheromone concentration to graph G. The optimal route of emergency evacuation in the campus can be obtained when the importance of trip distance is above 5 and the degree of conformity is above 0.3; while the worse-case route is obtained with the importance of trip distance above 5 and the degree of conformity below 0.5.

4

Research on optimization model of marine industry strategic adjustment under complex maritime conditions based on ant colony algorithm

Shen X.

Polish Maritime Research

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2018

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S 2

164--169

EN

This article seeks to analyze the factors constraining the development of strategic marine emerging industries and the deficiencies in China’s strategic marine emerging industry development policies. Learn from the successful experiences of overseas strategic marine emerging industry development policies. We will study and construct a policy framework for the development of strategic marine emerging industries in the new era, guided by the scientific concept of development and enhanced by the capacity of independent innovation. Provide policy recommendations for actively promoting the development of strategic marine emerging industries. At the same time, it provides theoretical and methodological reference for the formulation and implementation of China’s strategic marine emerging industry policies. On the basis of reviewing relevant theories of industrial policy, this article first defines China’s strategic emerging industries and clarifies the connotation of China’s strategic marine emerging industry development policies. Then, the paper conducts detailed analysis on the development policies of strategic marine emerging industries at home and abroad, and summarizes the experience of overseas strategic development policies for marine emerging industries. Finally, combining the above-mentioned comprehensive analysis, with the guidance of the scientific concept of development, the development strategy and concrete development policy of China’s strategic emerging industries in the ocean are proposed.

5

Use of Monte Carlo Simulation to Optimize the Loading Pattern Using an Ant Colony Algorithm

Stanisz P, Oettingen M.

Logistyka

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2014

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nr 4

4927--4933, CD6

EN

This paper deals with loading pattern optimization that is logistic domain in nuclear reactors. To find the best distribution we created algorithm based on a recent method the Ant Colony Optimization (ACO) algorithm, which is used in transport networks. In our work we used the Monte Carlo methods witch the SERPENT code. This method provided well estimated multi-group cross sections. Our model, which was described by a cross section representation, was handled by the ACO algorithm coupled with the PARCS code. The final result shows convergence of our calculations. Cooperation of these three methods have been determined and presage more detailed study in future. This paper describes the methodology, with some final results obtained by the ACO algorithm through Monte Carlo calculations and Core simulation.

PL

Artykuł dotyczy optymalizacji załadunku paliwa bazującego na obliczeniach Monte Carlo. Optymali-zacja załadunku jest zagadnieniem logistycznym. Do znalezienia najlepszego rozkładu, stworzyliśmy własny algorytm bazujący na obiecujących wynikach uzyskiwanych za pomocą Algorytmu Kolonii Mrówek (AKM) szeroko wykorzystywanego w transporcie. Do otrzymania odpowiednich wyników wspiera-liśmy się programem Monte Carlo SERPENT, dzięki któremu otrzymaliśmy wielogrupowe przekroje czynne dla określonych kaset paliwowych. Następnie nasz model, był wykorzystywany przez program PARCS i zarządzającym nim, napisanym przez nas programem. Ostateczne rezultaty potwierdzają asymptotyczną zbieżność naszych wyników. Została osiągnięta współpraca trzech metod obliczeniowych. Artykuł przedstawia metodologię wraz z niektórymi rezultatami otrzymanymi za pomocą wyżej wymienionymi programami.

6

Lokalizacja baterii kondensatorów w sieci elektroenergetycznej – wykorzystanie algorytmu mrówkowego

Wilkosz K., Mytych P.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 10

22-25

PL

W pracy przedstawiana jest metoda znajdowania optymalnych lokalizacji i parametrów baterii kondensatorów w systemie elektroenergetycznym z wykorzystaniem algorytmu mrówkowego. Cechą charakterystyczną metody jest wykorzystanie w procesie znajdowania rekomendowanych do zainstalowania baterii kondensatorów oszacowań udziałów tych baterii w redukcji systemowych strat energii czynnej.

EN

In the paper, the method of searching optimal localizations and parameters of capacitor banks in a power system with the use of ant colony algorithm is presented. The characteristic feature of the method is that in the process of searching capacitor banks, which will be recommended for installation, assessments of contribution of particular capacitor banks to reduction of system active energy losses are utilized.

7

A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO

Nagaraj B., Vijayakumar P.

Journal of Automation Mobile Robotics and Intelligent Systems

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2011

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Vol. 5, No. 2

42-48

EN

Proportional - Integral - Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. How ever PID controller are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This article comes up with a hybrid approach involving Genetic Algorithm (GA), Evolutionary Pro gramming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The proposed hybrid algorithm is used to tune the PID parameters and its per formance has been compared with the conventional me thods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. Speed control of DC motor process is used to assess the efficacy of the heuristic algorithm methodology

8

Usprawnianie procesów transportowych z wykorzystaniem usług sieciowych na przykładzie przedsiębiorstwa komunalnego

Karkula M., Szymanowska A.

Logistyka

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2010

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nr 2

PL

Celem artykułu jest analiza wybranych procesów z zakresu transportu miejskiego i propozycja nowego rozwiązania, które ma za zadanie zminimalizować liczbę przejazdów w obszarach aglomeracji oraz odciążyć miasto od zbędnego transportu. Zaproponowane rozwiązanie łączy wykorzystanie metod optymalizacji kombinatorycznej z usługami sieciowymi Google Maps.

EN

Aim of this study is to analyze selected processes in the field of urban transport and to propose a new solution that aims to minimize the number of trips in urban areas and relieve the city from the unnecessary transport. The proposed solution combines the use of combinatorial optimization methods and web services such as Google Maps.

9

The search for minimum potential energy structures of small atomic clusters. Application of the ant colony algorithm.

Raczyński P., Gburski Z.

Materials Science Poland

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2005

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Vol. 23, No. 3

599--606

EN

The ant colony algorithm has been applied to the problem of finding the minimal potential energy configuration of a small physical system (cluster) of atoms interacting via the Lennard-Jones phenomenological potential. The ants were positively motivated if their activity (displacement of atomic positions) leads to a lower total potential energy of the system. Starting from a random spatial distribution of atoms, during the optimalization process, the ants were able to find configurations with energies much lower than the initial ones. The optimized configurations generated by the ant colony algorithm can be used as a good starting point for classical or ab initio molecular dynamics (MD) simulations.