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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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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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A bi-objective coordination setup problem in a two-stage production system

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第3期189卷 734-745页

作者： Ciavotta, Michele Detti, Paolo Meloni, Carlo Pranzo, Marco Univ Roma Tre DIA I-00146 Rome Italy Univ Siena DII I-53100 Siena Italy Politecn Bari DEE I-70125 Bari Italy

This paper addresses a problem arising in the coordination between two consecutive departments of a production system, where parts are processed in batches, and each batch is characterized by two distinct attributes. Due to the lack of interstage buffering between the two stages, these departments have to follow the same batch sequence. In the first department, a setup occurs every time the first attribute of a new batch is different from the one of the previous batch. In the downstream department, there is a setup when the second attribute changes in two consecutive batches. The problem consists in finding a batch sequence optimizing the number of setups paid by each department. This case results in a particular bi-objective combinatorial optimization problem. We present a geometrical characterization for the feasible solution set of the problem, and we propose three effective heuristics, as shown by an extensive experimental campaign. The proposed approach can be also used to solve a class of single-objective problems, in which setup costs in the two departments are general increasing functions of the number of setups. (C) 2006 Elsevier B.V. All rights reserved.

关键词： setup minimization multi-objective algorithms sequencing manufacturing systems

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

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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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A novel multi-objective directed bee colony optimization algorithm for multi-objective emission constrained economic power dispatch

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 1241-1250页

作者： Kumar, Rajesh Sadu, Abhinav Kumar, Rudesh Panda, S. K. Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

In this paper, a multi-objective directed bee colony optimization algorithm (MODBC) is comprehensively developed and successfully applied for solving a multi-objective problem of optimizing the conflicting economic dispatch and emission cost with both equality and inequality constraints is showcased. Classical optimization techniques like direct search and gradient methods fail to give the global optimum solution. The proposed algorithm is an integration of the deterministic search, the multi-agent system (MAS) environment and the bee decision-making process. Thus making use of deterministic search, multi-agent environment and bee swarms, the MODBC realizes the purpose of optimization. The hybridization makes MODBC to obtain a unique and fast solution and hence generate a better pareto front for multi-objective problems. The above mentioned multi-objective evolutionary algorithms have been applied to the standard IEEE 30 bus six generator test system. Results of the proposed algorithm have been compared with traditional methods like linear programming (LP) and multi-objective stochastic search technique (MOSST). The performance of the introduced algorithm is also compared with other evolutionary algorithms like Non-dominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA) and Strength Pareto Evolutionary Algorithm (SPEA) and Particle Swarm Optimization (PSO). The results show the robustness and accuracy of the proposed algorithm over the traditional methods and its other multi-objective evolutionary algorithm (MOEA) counterparts. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Evolutionary algorithms Economic load dispatch multi-objective algorithms

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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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General Subpopulation Framework and Taming the Conflict Inside Populations

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EVOLUTIONARY COMPUTATION 2015年第1期23卷 1-36页

作者： Vargas, Danilo Vasconcellos Murata, Junichi Takano, Hirotaka Botazzo Delbem, Alexandre Claudio Kyushu Univ Grad Sch Informat Sci & Elect Engn Fukuoka 8190395 Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

Structured evolutionary algorithms have been investigated for some time. However, they have been under explored especially in the field of multi-objective optimization. Despite good results, the use of complex dynamics and structures keep the understanding and adoption rate of structured evolutionary algorithms low. Here, we propose a general subpopulation framework that has the capability of integrating optimization algorithms without restrictions as well as aiding the design of structured algorithms. The proposed framework is capable of generalizing most of the structured evolutionary algorithms, such as cellular algorithms, island models, spatial predator-prey, and restricted mating based algorithms. Moreover, we propose two algorithms based on the general subpopulation framework, demonstrating that with the simple addition of a number of single-objective differential evolution algorithms for each objective, the results improve greatly, even when the combined algorithms behave poorly when evaluated alone at the tests. Most importantly, the comparison between the subpopulation algorithms and their related panmictic algorithms suggests that the competition between different strategies inside one population can have deleterious consequences for an algorithm and reveals a strong benefit of using the subpopulation framework.

关键词： Structured evolutionary algorithms parallel evolutionary algorithms hybridization multi-objective algorithms novelty search general subpopulation framework general differential evolution

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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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On Homogenization of Coal in Longitudinal Blending Beds 14

On Homogenization of Coal in Longitudinal Blending Beds

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Shukla, Pradyumn Kumar Cipold, Michael P. Karlsruhe Inst Technol D-76021 Karlsruhe Germany J&C Bachmann GmbH Bad Wildbad Germany

ISBN: (纸本)9781450326629

Coal blending processes mainly use static and non-reactive blending methods like the well-known Chevron stacking. Although real-time quality measurement techniques such as online X-ray fluorescence measurements are available, the possibility to explore a dynamic adaptation of the blending process to the current quality data obtained using these techniques has not been explored. A dynamic adaptation helps to mix the coal from different mines in an optimal way and deliver a homogeneous product. The paper formulates homogenization of coal in longitudinal blending beds as a bi-objective problem of minimizing the variance of the cross-sectional quality and minimizing the height variance of the coal heap in the blending bed. We propose a cone based evolutionary algorithm to explore different trade-off regions of the Pareto front. A pronounced knee region on the Pareto front is found and is investigated in detail using a knee search algorithm. There are many interesting problem insights that are gained by examining the solutions found in different regions. In addition, all the knee solutions outperform the traditional Chevron stacking method.

关键词： multi-objective algorithms Knees Simulation optimization

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A Search-Based Approach for Software Product Line Design 14

A Search-Based Approach for Software Product Line Design

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18th International Software Product Line Conference (SPLC)

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina Gimenes, Itana M. S. Oizumi, Willian Nalepa Univ Fed Parana Curitiba Parana Brazil Univ Estadual Maringa Maringa Parana Brazil

ISBN: (纸本)9781450327404

The Product Line Architecture (PLA) can be improved by taking into account key factors such as feature modularization, and by continuously evaluating its design according to metrics. Search-Based Software Engineering (SBSE) principles can be used to support an informed-design of PLAs. However, existing search-based design works address only traditional software design not considering intrinsic Software Product Line aspects. This paper presents MOA4PLA, a search-based approach to support the PLA design. It gives a multi-objective treatment to the design problem based on specific PLA metrics. A metamodel to represent the PLA and a novel search operator to improve feature modularization are proposed. Results point out that the application of MOA4PLA leads to PLA designs with well modularized features, contributing to improve features reusability and extensibility. It raises a set of solutions with different design trade-offs that can be used to improve the PLA design.

关键词： Software product lines multi-objective algorithms search-based PLA design

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