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1

MOPOA: A New Multi-Objective Pufferfish Optimization Algorithm

Nasrabadi Mohaddethe, Khazaiepoor Mahdi, Kherad Mahdi

Foundations of Computing and Decision Sciences

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2026

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Vol. 51, No. 1

139--170

EN

Multi-objective optimization problems (MOPs) pose significant challenges due to the presence of multiple conflicting objectives. This paper introduces MOPOA, a novel Multi-Objective Pufferfish Optimization Algorithm inspired by the defensive behaviors of pufferfish in nature. MOPOA extends the original single-objective POA by incorporating Pareto dominance, an external archive for preserving non-dominated solutions, and a crowding distance mechanism to maintain solution diversity. The algorithm balances exploration and exploitation through biologically inspired phases simulating predator-prey interactions. To evaluate MOPOA's performance, it was benchmarked against several state-of-the-art algorithms, including NSGA-III, MOPSO, MODA, and MOFDO, on two well-known test suites: the ZDT and CEC-2019 multi-objective functions. Results indicate that MOPOA not only achieves superior convergence to the Pareto front but also maintains high diversity and robustness across diverse optimization scenarios. These findings position MOPOA as a powerful and adaptive tool for solving complex real-world multi-objective problems.

2

Minimalizacja asymetrii w sieci niskiego napięcia zawierającej mikroinstalacje fotowoltaiczne oraz magazyny energii

Pijarski Paweł, Przepiórka Dominik, Binkowski Tomasz

Rynek Energii

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2025

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Nr 5

3--9

PL

W artykule przedstawiono analizy dotyczące asymetrii napięć i prądów w sieci niskiego napięcia, wynikające z nierównomiernego rozmieszczenia odbiorników jednofazowych oraz mikroinstalacji fotowoltaicznych. Zastosowanie pięciu różnych, metaheurystycznych algorytmów optymalizacyjnych pozwoliło na minimalizację asymetrii poprzez optymalne sterowanie mocą czynną i bierną w poszczególnych fazach. Wyniki symulacji, przeprowadzonych na zmodyfikowanej sieci testowej IEEE ELVTF, potwierdziły efektywność algorytmów optymalizacyjnych oraz znaczenie asymetrycznej regulacji. Zaproponowane podejście może przyczynić się do poprawy jakości zasilania w warunkach rosnącej liczby odnawialnych źródeł energii.

EN

The paper presents an analysis of voltage and current asymmetry in low-voltage networks, caused by the uneven distribution of single-phase loads and photovoltaic micro-installations. The application of ﬁve different metaheuristic optimization algorithms enabled the minimization of asymmetry through optimal control of active and reactive power in individual phases. Simulation results, carried out using a modified IEEE ELVTF test network, conﬁrmed the effectiveness of the optimization algorithms and the importance of asymmetric regulation. The proposed approach may contribute to improving power quality under the growing presence of renewable energy sources.

3

Optimisation of low voltage network operation - selected power quality indicators vs. metaheuristic optimisation algorithms

Pijarski Paweł, Przepiórka Dominik, Malska Wiesława

Przegląd Elektrotechniczny

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2025

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R. 101, nr 6

63--68

EN

The article analyses selected power quality indicators in a low-voltage network containing RES sources and energy storage. Their optimal values were determined using ten different metaheuristic optimisation methods. The proposed analyses will enable the selection of the most beneficial indicator from the point of view of managing the operation of the distribution network through optimal control of the charging power of energy storage and the reactive power of prosumer photovoltaic installations. This will also reduce power losses and equalise voltage profiles.

PL

W artykule przeanalizowano wybrane wskaźniki jakości zasilania w sieci niskiego napięcia zawierającej źródła OZE oraz magazyny energii. Wyznaczono ich optymalne wartości przy wykorzystaniu dziesięciu różnych metaheurystycznych metod optymalizacji. Proponowane analizy umożliwią wybór najbardziej korzystnego wskaźnika z punktu widzenia zarządzania pracą sieci dystrybucyjnej poprzez optymalne sterowanie mocą ładowania magazynów energii oraz mocą bierną prosumenckich instalacji fotowoltaicznych. Pozwoli to również ograniczyć straty mocy i wyrównać profile napięcia.

4

Optymalne planowanie budowlanego, wieloobiektowego przedsięwzięcia drogowego z użyciem algorytmu przeszukiwania genetycznego

Podolski Michał

Przegląd Budowlany

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2025

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R. 96, nr 1

112--116

PL

W artykule przedstawiono zagadnienie harmonogramowania budowlanego, wieloobiektowego przedsięwzięcia drogowego. Podczas wykonywania robót w takich przedsięwzięciach występują możliwości częściowego zazębiania się kolejnych czynności w obiektach. Ze względu na potrzebę maksymalnego skrócenia czasu zajęcia pracami budowlanymi poszczególnych obiektów zakłada się w nich ciągłość wykonywania robót. Założenia te prowadzą do zadania optymalizacyjnego polegającego na poszukiwaniu optymalnej kolejności wykonywania obiektów, która minimalizuje czas trwania przedsięwzięcia. W artykule to zagadnienie z powodzeniem rozwiązano za pomocą algorytmu przeszukiwania genetycznego i zilustrowano przykładem praktycznym.

EN

The article presents the issue of scheduling a multiunit road construction project. During the execution of works in such projects, there is a possibility of partial overlapping of successive activities in the units. Due to the need to maximally shorten the time of occupancy with construction works of the units, continuity of the works is assumed in them. These assumptions lead to the optimization task consisting in finding the optimal order of execution of the units that minimizes the duration of the project. In the article, this issue was successfully solved using a genetic search algorithm and illustrated by a case study.

5

Ensemble machine learning model prediction and metaheuristic optimisation of oil spills using organic absorbents : supporting sustainable maritime

Dung Le Quang, Pham Duc, Bui Thi Anh Em, Nquyen Lan Huong, Nguyen Phuoc Quy Phong, Dang Thanh Nam

Polish Maritime Research

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2025

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nr 2

141--155

EN

The study discusses the urgent necessity of environmentally friendly remedies to tackle the increasing frequency of oil spills. Conventional remedial oil leak techniques, such as mechanical recovery and chemical dispersants, may cause environmental damage and suffer from low effectiveness. Organic absorbents are environment-friendly and low-cost substitutes; however, their acceptance is hampered by inadequate performance and optimisation studies. To close this gap, our work combines metaheuristic algorithms with ensemble machine learning and suggests a hybrid technique for the precise prediction and improvement of oil removal efficiency. Using Random Forest (RF) and XGBoost models, high R2 values (RF: 0.9517–0.9559; XGBoost: 0.9760), minimal errors, and strong generalisation were obtained by predictive modelling. Operating conditions were optimised using Grey Wolf Optimisation (GWO), showing an optimal percentage of oil removed (POR) of 93.59%. Combining metaheuristics with machine learning ensures accuracy and practical results, tackling the complexity of oil spill control. By concentrating on organic absorbents, the study fits with worldwide sustainability initiatives and provides a useful foundation for actual implementation.

6

A Comprehensive Study of Metaheuristic Performance on Job-Shop Scheduling Problems

Sousa Tomás, Santos André S., Varela Leonilde R., Trojanowska Justyna

Management and Production Engineering Review

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2025

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Vol. 16, No. 4

[nr art. 7], s. 1--9

EN

The article presents an in-depth literature review on the performance of metaheuristics in operations scheduling problems and aims to evaluate the use of metaheuristics, concerning job-shop scheduling problems. In the first part, a literature review was conducted on the significance of operations scheduling and its different types, as well as metaheuristics and jobshop scheduling problems, providing historical context to the three topics. The methodology for the selection of the papers included in the bibliometric study is explained. Twenty articles from Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Tabu Search, addressing job-shop problems were selected. Then, various statistical analyses were conducted, such as the analysis of the evolution of results throughout the years and the performance comparison analysis between metaheuristics. Finally, a discussion about the results obtained is held, presenting the conclusions. The statistical analyses revealed that the performance of metaheuristics depends on multiple factors and that their evaluation should not be carried out in isolation. In terms of practical results, the analysis showed that Genetic Algorithms achieved the highest average makespan reduction, followed by Simulated Annealing, Particle Swarm Optimization, and Tabu Search. For example, GA consistently reduced makespan by more than 15% compared to industrial cases, while Tabu Search showed the least consistent performance across studies.

7

Grey wolf optimization algorithm for a concurrent real‐time optimization problem in game theory

Górski Adam M., Ogorzałek Maciej

Journal of Automation Mobile Robotics and Intelligent Systems

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2025

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Vol. 19, No. 2

65--72

EN

This paper presents a grey wolf algorithm for a concurrent real-time optimization problem in searching for an optimal game-solving solution. There are many solutions to the game. Each solution can demand different optimal values of different parameters. However, some ways the players try to solve the game do not lead to success. The optimization problem consists of two phases. Each phase impacts the second one in real time. The first phase is responsible for the optimization of the parameters. The second phase validates the choice and optimizes the parameters. As an optimization method, we chose grey wolf optimization. At the beginning, the algorithm generates several solutions. The solution with the value of the parameters closest to maximum is the position of an alpha wolf. The rest of the solutions are, according to the values of the parameters, split into the positions of beta, delta, and omega wolves.

8

Portfolio optimization with translation of representation for transport problems

Zajecka Małgorzata, Mastalerczyk Mateusz, Chong Siang Yew, Yao Xin, Kwiecien Joanna, Chmiel Wojciech, Dajda Jacek, Kisiel-Dorohinicki Marek, Byrski Aleksander

Journal of Artificial Intelligence and Soft Computing Research

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2025

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Vol. 15, No. 1

57--75

EN

The paper presents a hybridization of two ideas closely related to metaheuristic computing, namely Portfolio Optimization (researched by Xin Yao et al.) and Translation of Representation for different metaheuristics (researched by Byrski et al.). Thus, difficult problems (discrete optimization) are approached by a sequential run through a number of steps of different metaheuristics, providing the translation of representation (since the algorithms are completely different). Therefore, close cooperation of e.g. ACO, PSO, and GA is possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.

9

Multi-criteria vehicle routing problem for a real-life parcel locker-based delivery

Idzikowski Radosław, Rudy Jarosław, Jaroszczuk Michał

International Journal of Electronics and Telecommunications

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2025

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Vol. 71, No. 3

art. no. 17, s. 1-9

EN

In this paper, a multi-criteria Vehicle Routing Problem with distance and capacity constraints for modelling a delivery system with parcel locker, is considered. The problem is formulated and two optimization criteria are defined. The first criterion minimizes the total travel time of all vehicles and the second criterion minimizes the total penalty for late delivery of orders. Three solving methods, relying on the concept of Pareto-optimality, are proposed: a greedy constructive heuristic, a Tabu Search metaheuristic and a Genetic Algorithm. A number of benchmark instances are created using real-life parcel locker locations and traveling times, from one of the major cities in Poland. In preliminary research, sorting strategies for the greedy method are tested, with the sorting based on deadline-arrival difference to priority ratio yielding the best performance in all tested cases. Next, computer experiments are performed to evaluate the quality of the proposed methods, using the concept of Hypervolume Indicator. Results confirm that both Tabu Search and Genetic Algorithm significantly improve the solution provided by the greedy algorithm, with Genetic Algorithm being the most effective on average. However, results also indicate that both Tabu Search and Genetic Algorithm have different effectiveness in different cases. It is concluded that the best performance is achieved by both algorithms being used in parallel, complementing each other.

10

The use of artificial intelligence in building information modelling: document-based bibliographic analysis

Zima Krzysztof

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

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2025

|

Vol. 14

86--94

EN

A strong connection to the architectural, engineering and construction network, with access to technologies such as BIM, leads to increased industrial productivity. The use of BIM can be implemented by using many programs and systems that provide knowledge and quick solutions. Combining BIM with artificial intelligence (AI) methods can provide even greater software benefits. This article examines the use of AI methods coupled with BIM in scientific research. The main objective of the research is to learn about trends in the use of artificial intelligence in BIM research in the context of construction management. This was achieved through bibliometric and scientifically accessible searches and mapping with text mining. Data on artificial intelligence in BIM research has been collected by reviewing and using articles from the Scopus database. The paper will contribute to the access of literature and trends in AI and BIM research.

11

(µ + λ) evolution strategy with socio-cognitive mutation

Urbanczyk Aleksandra, Kucaba Krzysztof, Wojtulewicz Mateusz, Kisiel-Dorohinicki Marek, Rutkowski Leszek, Duda Piotr, Kacprzyk Janusz, Yao Xin, Chong Siang Yew, Byrski Aleksander

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

|

Vol. 18, No. 1

1--11

EN

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

12

Influence of migration on efficacy and efficiency of parallel evolutionary computing

Biełaszek Sylwia, Rutkowski Leszek, Byrski Aleksander

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

|

Vol. 18, No. 4

1--12

EN

Metaheuristics, such as evolutionary algorithms (EAs), have been proven to be (also theoretically, see, for example, the works of Michael Vose [1]) universal optimization methods. Previous works (Zbigniew Skolicki and Kenneth De Jong [2]) investigated impact of migration intervals on island models of EAs in their works. Here we explore different migration intervals and amounts of migrating individuals, complementing Skolicki and DeJong’s research. In our experiments, we use different ways of selecting migrants and pave the way for further research, e.g., involving different topologies and neighborhoods. We present the idea of the algorithm, show experimental results.

13

A hybrid equilibrium optimizer based on moth flame optimization algorithm to solve global optimization problems

Wang Zongshan, Ala Ali, Liu Zekui, Cui Wei, Ding Hongwei, Jin Gushen, Lu Xu

Journal of Artificial Intelligence and Soft Computing Research

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2024

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Vol. 14, No. 3

207--235

EN

Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

14

New ideas in Lagrangian relaxation for a scheduling problem with the weighted tardiness criterion

Rudy Jarosław, Idzikowski Radosław, Smutnicki Czesław, Banaszak Zbigniew, Bocewicz Grzegorz

International Journal of Applied Mathematics and Computer Science

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2024

|

Vol. 34, no. 2

235--251

EN

We consider an extension of Lagrangian relaxation methods for solving the total weighted tardiness scheduling problem on a single machine. First, we investigate a straightforward relaxation method and decompose it into upper and lower subproblems. For the upper subproblem we propose an alternative solving method in the form of a local search metaheuristic. We also introduce a scaling technique by arbitrary numbers to reduce the complexity of the problem and confront it with greatest common divisor scaling. Next, we propose a novel alternative relaxation approach based on aggregating constraints. We discuss the properties and implementation of this new approach and a technique to further reduce its computational complexity. We perform a number of computer experiments on instances based on the OR-Library generation scheme to illustrate and ascertain the numerical properties of the proposed methods. The results indicate that for larger instances the proposed alternative relaxation and scaling approaches have a much better convergence rate with little to no decrease in solution quality. The results also show that the proposed local-search metaheuristic is a viable alternative to the existing solving methods.

15

A novel chaotic binary butterfly optimization algorithm based feature selection model for classification of autism spectrum disorder

Ramakrishnan Anandkumar, Ramalingam Rajakumar, Ramalingam Padmanaban, Ravi Vinayakumar, Alahmadi Tahani Jaser, Maidin Siti Sarah

International Journal of Applied Mathematics and Computer Science

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2024

|

Vol. 34, no. 4

647--660

EN

Autism spectrum disorder (ASD) issues formidable challenges in early diagnosis and intervention, requiring efficient methods for identification and treatment. By utilizing machine learning, the risk of ASD can be accurately and promptly evaluated, thereby optimizing the analysis and expediting treatment access. However, accessing high dimensional data degrades the classifier performance. In this regard, feature selection is considered an important process that enhances the classifier results. In this paper, a chaotic binary butterfly optimization algorithm based feature selection and data classification (CBBOAFS-DC) technique is proposed. It involves, preprocessing and feature selection along with data classification. Besides, a binary variant of the chaotic BOA (CBOA) is presented to choose an optimal set of a features. In addition, the CBBOAFS-DC technique employs bacterial colony optimization with a stacked sparse auto-encoder (BCO-SSAE) model for data classification. This model makes use of the BCO algorithm to optimally adjust the ‘weight’ and ‘bias’ parameters of the SSAE model to improve classification accuracy. Experiments show that the proposed scheme offers better results than benchmarked methods.

16

CV19T, a novel bio-socially inspired method, belonging to a new nature-inspired metaheuristics class

Bouthina Saib, Abdessemed Mohamed-Rida, Hocine Riadh

Computer Science

|

2024

|

T. 25 (3)

351--396

EN

The paper presents CV19T, a novel bio-socially inspired metaheuristic, where the cornerstone on which rests is the relationship between humans crowding density, on one side, influenced by their mobility, mutual attractiveness to each other and individual consciousness, and on the other side, the amazing speed of COVID-19 propagation. CV19T originality resides in the fact of combining features from two completely distinct and famous classes, namely: swarm intelligence and Evolutionary Algorithms. Moreover, CV19T extends elitism concept (i.e. survival of the most powerful), on which are based courant evolutionist approaches to the survival of the most beneficial one. Also, CV19Tshows that additional parameters can increase control of its behaviour, in many cases, leading to rise in its results relevance. To validate CV19T, it was tested on benchmarks set, including 23 functions (unimodal, multimodal and fixeddimensional multimodal) and 4 real-world problems.

17

Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study

Sahar Habbadi, Herrou Brahim, Sekkat Souhail

Management and Production Engineering Review

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2023

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Vol. 14, No. 3

44--56

EN

The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.

18

Application of the path-repairing technique and virus optimization algorithm for the dimensional synthesis of four-bar mechanisms

Grabski Jakub Krzysztof, Sopa Martyna, Mrozek Agata

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e134, 2023

EN

This paper considers the synthesis of the four-bar mechanism. It is treated here as an optimization problem, in which an objective function is defined. To solve this problem, a metaheuristic called the virus optimization algorithm is employed. Furthermore, a new path-repairing technique recently published by Sleesongsom and Bureerat is applied instead of the very common technique related to the application of a penalty function. This makes the search by means of the metaheuristic more efficient. Furthermore, the obtained results are very accurate.

19

Comparative Analysis of Exact, Heuristic and Metaheuristic Algorithms for Flexible Assembly Scheduling

Maghiar Octavian, Selea Teodora, Copie Adrian, Micota Flavia, Marin Mircea

Annals of Computer Science and Information Systems

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2023

|

Vol. 35

615--625

EN

Real-world manufacturing scenarios usually lead to difficult assembly scheduling problems. Besides strict precedence constraints between jobs or operations, such problems incorporate constraints related to maintenance activities on working stations (machines) and specific setup times when different operations are executed on the same machine. This paper analyzes the performance of several approaches, based on mathematical programming and on (meta)heuristics, to solve flexible assembly scheduling problems characterized by an arbitrary tree-like structure of the operation network. In this context, a specific encoding of candidate solutions and some specific perturbation operators are proposed. The encoding and the operators allows the distribution of sub(batches) of operations on several machines which leads, for some assembly scheduling problems, to a significant decrease of the makespan.

20

Ant Colony Optimization for Workforce Planning with Hybridization

Fidanova Stefka, Ganzha Maria

Annals of Computer Science and Information Systems

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2023

|

Vol. 35

955--959

EN

Production organization plays a key role in the success of any enterprise. Workforce planning and assignment is an important element of the production organization. Optimizing workforce planning can improve the overall organization of production. The main goal is to minimize the assignment cost of the workers who will perform the planned work. The problem is known to be NP-hard, therefore we will apply methods from the field of artificial intelligence. The problem is to select workers to be assigned to perform the jobs. This is a difficult optimization problem with very strict constraints. For this reason, most of the existing methods hardly find feasible solutions. We propose Ant Colony Optimization Algorithm with hybridization, combination with local search procedures. We compare and analyze their performance.