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1
Content available Optimizing the cost of a school building using the Rao algorithm
Aslay Semi Emrah, Dede Tayfun, Grzywiński Maksym
Budownictwo o Zoptymalizowanym Potencjale Energetycznym
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2024
|
Vol. 13
45--49
EN
In this study, cost optimization of a 4-storey school building is carried out. For the optimization, ACDOS (Automated Cost and Design Optimization of Structures) program – which is a computing platform created by the authors – is used. The Rao-1 algorithm is the optimization method used. As a result, a cost analysis of the RC building was performed and 12% cost savings were achieved.
2
Content available remote A review on research trends in using cuckoo search algorithm: applications and open research challenges
Safdar Kalsoom, Abdul Rani Khairul Najmy, Rahim Hasliza A., Rosli Siti Julia, Jamlos Mohd Aminudin
Przegląd Elektrotechniczny
|
2023
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R. 99, nr 5
18--24
EN
This paper provides an exclusive understanding of the Cuckoo Search Algorithm (CSA) using a comprehensive review for various optimization problems. CSA is a swarm-based nature inspired, intelligent and metaheuristic approach, which is used to solve complex, single or multi objective optimization problems to provide better solutions with maximum or minimum parameters. It was developed in 2009 by Yang and Deb to emulate the breeding behaviour of cuckoos. Since CSA provides promising solutions to solve real world optimization problems, in recent years there have been introduced several new modified and hybridized CSAs using for different applications. In this regard this article provides a comprehensive survey including recent trends, modifications, open research challenges, applications, and related taxonomies for various optimization problems. The literature of this reviewed paper belongs to the domains of engineering, optimization, and pattern recognition. The aim of this review paper is to provide a detailed overview regarding CSA for possible future directions using the recent contributions.
PL
Ten artykuł zapewnia wyłączne zrozumienie algorytmu przeszukiwania kukułki (CSA) za pomocą kompleksowego przeglądu różnych problemów optymalizacyjnych. CSA to oparte na roju, inteligentne i metaheurystyczne podejście inspirowane naturą, które służy do rozwiązywania złożonych, jedno- lub wielocelowych problemów optymalizacyjnych w celu zapewnienia lepszych rozwiązań z maksymalnymi lub minimalnymi parametrami. Został opracowany w 2009 roku przez Yang i Deb, aby naśladować zachowanie hodowlane kukułek. Ponieważ CSA zapewnia obiecujące rozwiązania do rozwiązywania rzeczywistych problemów optymalizacyjnych, w ostatnich latach wprowadzono kilka nowych zmodyfikowanych i hybrydowych CSA używanych do różnych zastosowań. Pod tym względem ten artykuł zawiera obszerną ankietę, w tym najnowsze trendy, modyfikacje, otwarte wyzwania badawcze, aplikacje i powiązane taksonomie dla różnych problemów optymalizacyjnych. Literatura tego recenzowanego artykułu należy do dziedzin inżynierii, optymalizacji i rozpoznawania wzorców. Celem tego artykułu przeglądowego jest przedstawienie szczegółowego przeglądu dotyczącego CSA dla możliwych przyszłych kierunków z wykorzystaniem ostatnich wkładów.
3
Content available Gold rush optimizer : a new population-based metaheuristic algorithm
Zolfi Kamran
Operations Research and Decisions
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2023
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Vol. 33, No. 1
113--150
EN
Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the proposed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.
4
Content available Hybrid Variable Neighborhood Search for solving school bus-driver problem with resource constraints
Ban Ha-Bang, Nguyen Hong-Phuong, Pham Dang-Hai
Computer Science
|
2023
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T. 24 (3)
297--325
EN
The school bus-priver problem with resource constraints (SBDP-RC) is an optimization problem with many practical applications. In the problem, several vehicles are prepared to pick a number of pupils in which the total resources of all vehicles are lower than a predefined value. The aim is to find a schedule that minimizes the sum of the pupils’ waiting times. The problem is NP-hard in the general case. In this paper, to solve the problem. After this, the post phase improves the solution by a general variable neighborhood search (GVNS) with a random neighborhood search combined with shaking technique. The proposed metaheuristic hybridization algorithm is tested on a benchmark to show its efficiency. The results show that the algorithm receives good feasible solutions fast. In many cases, better solutions can be found compared to previous metaheuristic algorithms.
5
Content available Estimation of air overpressure using bat algorithm
Dehghani Hesam, Ali Mohammad Nia Farid
Mining Science
|
2021
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Vol. 28
77-92
EN
Air overpressure (AOp) is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to nearby structures and to cause injuries, to personnel or animals, AOp is one of the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for AOp estimation. The scope of this study is to predict AOp induced by blasting through a novel approach based on the bat algorithm. For this purpose, the parameters of 62 blasting operations were accurately recorded and AOp were measured for each operation. In the next stage, a new empirical predictor was developed to predict AOp. The results clearly showed the superiority of the proposed bat algorithm model in comparison with the empirical approaches.
6
Content available remote Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization Framework
Zaborski Mateusz, Okulewicz Michał, Mańdziuk Jacek
Foundations of Computing and Decision Sciences
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2020
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Vol. 45, No. 3
234--254
EN
This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper. We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.
7
Content available Minimal cycle time determination and golf neighborhood generation for the cyclic flexible job shop problem
Bożejko W., Pempera J., Wodecki M.
Bulletin of the Polish Academy of Sciences. Technical Sciences
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2018
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Vol. 66, nr 3
333--344
EN
In the paper, a problem of scheduling operations in the cyclic flexible job shop system is considered. A new, very fast method of determining the cycle time for any order of tasks on machines is also presented. It is based on the analysis of the paths in the graph representing the examined problem. The theorems concerning specific properties of the graph are proven and used in the construction of the heuristic algorithm searching the solutions space by using the so-called golf neighborhood, which is generated in a way similar to the game of golf, which helps to intensify and diversify calculations. The conducted computational experiments fully confirmed the effectiveness of the proposed method. The proposed methods and properties can be adapted and used in the construction of local search algorithms for solving many other optimization problems.
8
Content available Using Template Method and Strategy Design Patterns in the Python Implementation of a Metaheuristic Algorithm for Solving Scheduling Problems
Figielska E.
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
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2017
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nr 17
7--22
EN
The paper shows how the Template Method and Strategy design patterns can be used in a program which solves different scheduling problems by means of a metaheuristic algorithm. The benefits offered by these design patterns as well as their drawbacks are discussed. An implementation example in the Python programming language is provided.
9
Using tabu search for feature selection in discriminant analysis
Stąpor K.
Studia Informatica
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2014
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Vol. 35, nr 4
45--58
EN
The well known statistical software packages like STATISTICA [11] continue to use classic variable selection methods in stepwise Discriminant Analysis such as the sequential forward/backward ones. Such stepwise procedures suffer from the nesting effect. Moreover, due to the criterion used for evaluation of variable subsets they are designed for descriptive purposes, not for predictive ones. We propose the new solution to the mentioned problems, the feature selection algorithm based on metaheuristic tabu search. After performing some tests it is found that our tabu search-based algorithm obtains significantly better results than stepwise procedures of statistical package.
PL
W znanych szeroko pakietach do obliczeń statystycznych (np. STATISTICA [11]) selekcja zmiennych wejściowych w module krokowej Analizy Dyskryminacyjnej wykonywana jest z wykorzystaniem klasycznych metod sekwencyjnych w przód/w tył, których wadą jest efekt zagnieżdżania. Również kryterium ewaluacyjne w tychże metodach jest dostosowane do celów deskryptywnych, a nie predyktywnych. Artykuł proponuje nowe rozwiązania wspomnianych problemów – algorytm selekcji z wykorzystaniem metaheurystyki przeszukiwania z tabu. Wykonane, wstępne testy wykazały znacznie lepszą sprawność klasyfikacji w porównaniu z metodami krokowymi.
10
Content available remote Algorytmy inspirowane naturą w kryptoanalizie
Polak I., Boryczka M.
Studia Bezpieczeństwa Narodowego
|
2014
|
R. 4, Nr 6
185--197
PL
W dzisiejszych czasach ochrona informacji jest niezwykle istotna, a jednym z elementów zapewniających ową ochronę jest kryptografia. Tu z kolei ważną rolę odgrywa kryptoanaliza, która pozwala badać bezpieczeństwo używanych szyfrów. Oprócz typowo analitycznego podejścia do łamania szyfrów (jak kryptoanaliza różnicowa, kryptoanaliza liniowa czy analiza statystyczna) od kilkunastu lat do tego celu zaprzęga się różnego rodzaju niedeterministyczne systemy inspirowane naturą. Użycie takich technik nie jest do końca intuicyjne – w kryptoanalizie często ważne jest znalezienie jednego konkretnego klucza (rozwiązania optymalnego), a każde inne rozwiązanie daje kiepskie rezultaty, nawet jeśli jest blisko optimum globalnego.
EN
Nowadays protection of information is very crucial and cryptography is a significant part of keeping information secure. Here in turn cryptanalysis plays an important role by examining the safety of ciphers used. Besides the analytical approach to ciphers breaking (eg. differential cryptanalysis, linear cryptanalysis, statistical analysis) for this purpose there are several kinds of non-deterministic, inspired by nature systems applied. It is not intuitive - as in cryptanalysis often it is important to find the exact key used (optimal solution) and every other solution is giving poor results, even if it is near global optimum.
11
Content available Metaheuristic optimization of marginal risk constrained long - short portfolios
Vijayalakshmi Pai G. A., Michel T.
Journal of Artificial Intelligence and Soft Computing Research
|
2012
|
Vol. 2, No. 3
259--274
EN
The problem of portfolio optimization with its twin objectives of maximizing expected portfolio return and minimizing portfolio risk renders itself difficult for direct solving using traditional methods when constraints reflective of investor preferences, risk management and market conditions are imposed on the underlying mathematical model. Marginal risk that represents the risk contributed by an asset to the total portfolio risk is an important criterion during portfolio selection and risk management. However, the inclusion of the constraint turns the problem model into a notorious non-convex quadratic constrained quadratic programming problem that seeks acceptable solutions using metaheuristic methods. In this work, two metaheuristic methods, viz., Evolution Strategy with Hall of Fame and Differential Evolution (rand/1/bin) with Hall of Fame have been evolved to solve the complex problem and compare the quality of the solutions obtained. The experimental studies have been undertaken on the Bombay Stock Exchange (BSE200) data set for the period March 1999-March 2009. The efficiency of the portfolios obtained by the two metaheuristic methods have been analyzed using Data Envelopment Analysis.
12
Content available Parallel Computation Approaches to Optimize Learning Systems
Czyż T., Rudek R., Selvaraj H.
International Journal of Electronics and Telecommunications
|
2011
|
Vol. 57, No. 2
223-228
EN
This paper is devoted to the total tardiness minimization scheduling problem, where the efficiency of a processor increases due to its learning. Such problems model real-life settings that occur in the presence of a human learning (industry, manufacturing, management) and in some computer systems. However, the increasing growth of significant achievements in the field of artificial intelligence and machine learning is a premise that the human-like learning will be present in mechanized industrial processes that are controlled or performed by machines as well as in the greater number of multi-agent computer systems. Therefore, the optimization algorithms dedicated in this paper for scheduling problems with learning are not only the answer for present day scheduling problems (where human plays important role), but they are also a step forward to the improvement of self-learning and adapting systems that undeniably will occur in a new future. To solve the analysed problem, we propose parallel computation approaches that are based on NEH, tabu search and simulated annealing algorithms. The numerical analysis confirm high accuracy of these methods and show that the presented approaches significantly decrease running times of simulated annealing and tabu search and also reduce the running times of NEH.
13
Asemblacja długich łańcuchów peptydowych przy wykorzystaniu metaheurystyki GRASP
Głowacki T., Kozak A., Formanowicz P.
Zeszyty Naukowe. Automatyka / Politechnika Śląska
|
2008
|
z. 151
203-208
PL
Ustalenie kolejności aminokwasów w cząsteczce białka nosi nazwę sekwencjonowania. Brak bezpośrednich metod sekwencjonowania długich peptydów powoduje, że potrzebne są dedykowane metody asemblacyjne, które odpowiednio poskładają krótkie łańcuchy w jeden długi łańcuch aminokwasów. W pracy tej został zaproponowany algorytm asemblacyjny typu GRASP. Przedstawiony algorytm został zaimplementowany i przetestowany dla zbioru rzeczywistych peptydów, a uzyskane rozwiązanie zostało przedyskutowane.
EN
Determining an order of amino acids in peptide structure is called sequencing method. Lack of direct sequencing methods for long peptides causes that assembling methods to combine many short peptides into one long structure are necessary. In this paper assembling algorithm based on GRASP method was proposed. The algorithm was implemented and tested on real peptides set and the obtained results was discussed.
14
Content available remote Fitness-distance analysis of a car sequencing problem
Kubiak M., Jaszkiewicz A., Kominek P.
Foundations of Computing and Decision Sciences
|
2006
|
Vol. 31, No. 3-4
263-276
EN
The paper describes a fitness-distance analysis of a car sequencing problem. It defines 5 similarity measures for solutions of the problem, describes computational experiments and provides values of determination coefficients between fitness and similarity, which are an indicator of fitness-distance correlation (or 'big valley'). The analysis reveals certain correlations of fitness and two types of similarity for 4 of 5 types of available instances. This results might motivate such designs of metaheuristics for these types of instances which would exploit the structure in fitness landscapes.
15
Content available remote Multiobjective simulated annealing - a metaheuristic technique for multiobjective optimization of a constrained problem
Suman B.
Foundations of Computing and Decision Sciences
|
2002
|
Vol. 27, No. 3
171-191
EN
The paper presents a multiobjective metaheuristic procedure - Weight based Multiobjective Simulated Annealing (WMOSA). The aim is to produce a set of potentially Pareto-optimal solutions of a constrained multiobjective optimization problem in a short time. In this method, the weight vector depends on the number of constraints to be satisfied by the solution vector and by the objective function vector, and the number of constraints of the problem. The weight vector is used in the acceptance criterion to handle constraints. Solution explores its neighborhood in a way similar to that of Classical Simulated Annealing. A computational experiment shows that WMOSA algorithm produces Pareto-optimal solutions of better quality than Suppapitanrm Multiobjective Simulated Annealing (SMOSA) with a penalty function approach at a lower computational cost.
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