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multi-objective Colliding Bodies Optimization Algorithm for the Obnoxious p-median Problems

PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2024年第3期68卷 705-710页

作者： Kaveh, Ali Mahdavi, Vahid Reza Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran *** Iran TU Wien Inst Struct Engn Res Unit Mech & Struct Dynam Karlsplatz 13-212-03 A-1040 Vienna Austria Arak Univ Technol Dept Civil & Geomech Engn Daneshgah St Arak *** Iran

The obnoxious p -median problem consists of locating p facilities among a set of sites such that the sum distance from any demand to its nearest facility and the dispersion among facilities are maximized. In this paper, the multi -objective colliding bodies optimization algorithm (MOCBO) is utilized to obtain the trade-off curve of the obnoxious p -median problems. The performance of the developed optimization method is investigated for locating obnoxious facilities through two case studies to maximize the two conflicting objectives. The performance of the MOCBO algorithm is further compared with those of the MPSO and NSGA-II algorithms representative of the state of the art in the field of multi -objective optimization. In this study, the MOCBO algorithm showed suitable convergence performance and generalization abilities compared to the MPSO and NSGA-II algorithms.

关键词： optimal locating obnoxious p -median colliding bodies optimization graph methods multi-objective algorithms

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QQLAOA: task scheduling with multi-objectives quantum mutation and Q-learning based arithmetic optimizer algorithm in cloud data centers

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COMPUTING 2025年第4期107卷 1-82页

作者： Mahjoub, Alireza Khalilian, Madjid Mohammadzadeh, Javad Islamic Azad Univ Dept Comp Engn Karaj Iran

Cloud computing enables scalable and flexible resource access with reduced costs and higher efficiency. However, challenges such as resource management, energy optimization, and load balancing require innovative scheduling approaches. This research proposes a multi-objective hybrid algorithm integrating reinforcement learning, quantum mutation (QM), and metaheuristic optimization to enhance task scheduling in cloud environments. The QM operator improves search diversity, while Q-learning dynamically balances exploration and exploitation. The proposed method is validated using statistical tests and benchmark datasets (GoCJ and synthetic data). Results show a makespan reduction of up to 22.04% and energy savings of 90.64%, demonstrating superior efficiency compared to existing methods.

关键词： Cloud computing Resource optimization Task scheduling multi-objective algorithms Energy consumption Load balancing

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Protein remote homology detection combining PCA and multiobjective optimization tools

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EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 67-76页

作者： Routray, Mukti Vipsita, Swati Int Inst Informat Technol Dept Comp Sci & Engn Bhubaneswar Odisha India

Protein remote homology detection (PRHD) is one of the intense researched and a complex problem in computational biology. The objective of PRHD is to predict the structural and functional characteristics of un-annotated protein sequences by means of homologies. Two sequences are said to be homologous if there exists a shared ancestry between them. It aims to detect the evolutionary relationships among protein sequences, that are distantly located, via computational techniques. Prediction of functions of protein sequences are computationally intensive. Features extracted from protein sequences are too high and presence of insignificant redundant features degrades the performance of computational model with respect to time and cost. Hence, using Principal Component Analysis (PCA, the high dimensional input matrix was transformed into a lower dimension matrix. In the first stage, for each protein sequence, 531 physico-chemical properties from AAIndex database are considered as features for homology detection. The protein sequences are taken from protein families from UniProtKB database. A set of 185 representative features have been extracted from 531 features using PCA. In the second stage, NSGA II and NSGA III, two powerful optimization techniques are used to efficiently search the non-zero eigen space and retrieve distinguishable eigen vectors. This work is tested on widely used Uniprot and SCOP benchmark datasets. To check the efficiency of both the algorithms, the controlling parameters are kept same and numerical simulations were performed. NSGA-III outperformed NSGA-II in the results.

关键词： Protein remote homology detection Physicochemical properties multi-objective algorithms

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An Enhanced multi-objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks

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IEEE ACCESS 2021年 9卷 149176-149195页

作者： Moshref, Mahmoud Al-Sayyed, Rizik Al-Sharaeh, Saleh Univ Jordan King Abdullah II Sch IT Dept Comp Sci Amman 11942 Jordan Univ Jordan King Abdullah II Sch IT Dept Informat Technol Amman 11942 Jordan

In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resource (battery), which requires considerable importance on deployment and routing strategies, to improve the performance of Quality of Service (QoS) in WSNs. Many of the existing strategies are based on metaheuristics algorithms such as Genetic algorithms to resolve the problem. This research proposes a new algorithm, Enhanced Non-Dominated Sorting Genetic Routing Algorithm (ENSGRA), to improve the QoS in WSNs. The proposed algorithm relies on Non-Dominated Sorting Genetic Algorithm 3 (NSGA-III), but adjusts reference points through the use of a dynamic weighted clustered scheduled vector to obtain new solutions. Moreover, ENSGRA can be used to find an integration between two parents crossover with multi-parent crossover (MPX), to produce multiple children and improve new offspring to obtain the optimal Pareto Fronts (PF). This algorithm excels when compared with the lagged multi-objective jumping particle swarm optimization, Non-dominated Sorting Genetic Algorithm-II and NSGA-III in terms of the QoS model (31% optimization percentage). Results show that the proposed ENSGRA is superior over other algorithms in evaluation measures for multi-objective algorithms.

关键词： Wireless sensor networks Sensors Quality of service Clustering algorithms Routing Sorting Heuristic algorithms Quality of service wireless sensor networks multi-objective algorithms clustering scheduling pareto front

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Toward text psychology analysis using social spider optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷 e6325-e6325页

作者： Hosseinalipour, Ali Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Khademi, Ali Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran Islamic Azad Univ Urmia Branch Dept Psychol Sci Orumiyeh Iran

Different nature-inspired meta-heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi-objective opposition-based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi-objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C-MEANS data clustering technique, a decision tree (DT), and Naive Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA-ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

关键词： feature selection meta-heuristic algorithm multi-objective algorithms social spider optimization algorithm text psychology analysis

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BPEL process defects prediction using multi-objective evolutionary search

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年第1期204卷

作者： Daaji, Marwa Ouni, Ali Gammoudi, Mohamed Mohsen Bouktif, Salah Mkaouer, Mohamed Wiem Univ Tunis El Manar Fac Sci Tunis Tunisia Univ Quebec ETS Montreal Montreal PQ Canada Univ Manouba Higher Inst Multimedia Art Manouba Tunisia UAE Univ Dept Comp Sci & Software Engn CIT Al Ain U Arab Emirates Rochester Inst Technol Rochester NY USA

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using multi-objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91%.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： BPEL process Anti-patterns multi-objective algorithms Genetic programming

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multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Poyatos, Javier Molina, Daniel Martinez-Seras, Aitor Del Ser, Javier Herrera, Francisco Univ Granada Andalusian Res Inst Data Sci & Computat Intelligen Dept Comp Sci & Artificial Intelligence Granada 18071 Spain TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain

Evolutionary Computation algorithms have been used to solve optimization problems in relation with architectural, hyper-parameter or training configuration, forging the field known today as Neural Architecture Search. These algorithms have been combined with other techniques such as the pruning of Neural Networks, which reduces the complexity of the network, and the Transfer Learning, which lets the import of knowledge from another problem related to the one at hand. The usage of several criteria to evaluate the quality of the evolutionary proposals is also a common case, in which the performance and complexity of the network are the most used criteria. This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm. MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show that our proposal achieves promising results in all the objectives, and direct relation are presented among them. The experiments also show that the most influential neurons help us explain which parts of the input images are the most relevant for the prediction of the pruned neural network. Lastly, by virtue of the diversity within the Pareto front of pruning patterns produced by the proposal, it is shown that an ensemble of differently pruned models improves the overall performance and robustness of the trained networks.(c) 2023 Published by Elsevier B.V.

关键词： Evolutionary Deep Learning multi-objective algorithms Pruning Out of Distribution detection Transfer Learning

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A Comparative Analysis of Two multi-objective Evolutionary algorithms in Product Line Architecture Design Optimization 26

A Comparative Analysis of Two Multi-Objective Evolutionary A...

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26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina State Univ Maringa UEM Dept Comp Sci Maringa Parana Brazil Fed Univ Parana UFPR Dept Comp Sci Curitiba Parana Brazil

ISBN: (纸本)9781479965731

The Product Line Architecture (PLA) design is a multi-objective optimization problem that can be properly solved with search-based algorithms. However, search-based PLA design is an incipient research field. Due to this, works in this field have addressed main points to solve the problem: adequate representation, specific search operators and suitable evaluation fitness functions. Similarly what happens in the search-based design of traditional software, existing works on search-based PLA design use NSGA-II, without evaluating the characteristics of this algorithm, such as the use of crossover operator. Considering this fact, this paper reports results from a comparative analysis of two algorithms, NSGA-II and PAES, to the PLA design problem. PAES was chosen because it implements a different evolution strategy that does not employ crossover. An experimental study was carried out with nine PLAs and results of the conducted study attest that NSGA-II performs better than PAES in the PLA design context.

关键词： software product line multi-objective algorithms product line architecture design

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Automation in electrifying flow organic synthesis

Tetrahedron Chem

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Tetrahedron Chem 2024年 12卷

作者： Hardwick, Tomas Ahmed, Nisar School of Chemistry Cardiff University Main Building Park Place Cardiff CF10 3AT United Kingdom

Organic electrochemical synthesis may be combined with continuous flow and automation technology. Here, the authors highlight the benefits of such chemical engineering approaches along with technology and software dir... 详细信息

关键词： Automation Flow electrochemistry Machine learning algorithms multi-objective algorithms Organic electrosynthesis

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An Enhanced multi-objective Particle Swarm Optimization in Water Distribution Systems Design

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WATER 2021年第10期13卷 1334页

作者： Torkomany, Mohamed R. Hassan, Hassan Shokry Shoukry, Amin Abdelrazek, Ahmed M. Elkholy, Mohamed Egypt Japan Univ Sci & Technol E JUST Environm Engn Dept Alexandria 21934 Egypt Alexandria Univ Irrigat Engn & Hydraul Dept Alexandria 11432 Egypt City Sci Res & Technol Applicat SRTA City Elect Mat Res Dept Adv Technol & New Mat Res Inst Alexandria 21934 Egypt E JUST Comp Sci & Engn Dept Alexandria 21934 Egypt Alexandria Univ Comp & Syst Engn Dept Alexandria 11432 Egypt

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

关键词： multi-objective algorithms network resilience particle swarm optimization water distribution systems

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