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Non-dominated Sorting Advanced Butterfly Optimization Algorithm for multi-objective problems

Journal of Bionic Engineering 2023年第2期20卷 819-843页

作者： Sushmita Sharma Nima Khodadadi Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili Department of Mathematics National Institute of Technology AgartalaTripura799046India Department of Civil and Environment Engineering Florida International University MiamiMiamiFL33199USA Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia***Iran Centre for Artificial Intelligence Research and Optimization Torrens UniversityFortitude ValleyQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea

This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization *** is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective *** to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance *** benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint *** performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance *** is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared *** all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.

关键词： multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance

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OPSBC: A method to sort Pareto-optimal sets of solutions in multi-objective problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Dosantos, Pelayo S. Bouchet, Agustina Marinas-Collado, Irene Montes, Susana Dept Stat & Operat Res & Math Didact C Leopoldo Calvo Sotelo 18 Oviedo 33007 Spain

In recent decades, the significance of multi -objective problems has grown substantially. One of the most popular methods for solving these problems involves the construction of Pareto sets. Pareto sets are exponentially sized relative to the input size of the problem, and so the need to reduce, or at least order, them arises. This work proposes the order of Pareto solutions by Borda count, an approach that makes use of ranking methods to establish preferences among the optimal solutions. To evaluate the proposed approach, a comparative study is conducted, evaluating its performance in comparison to other widely used and well -established methods within this domain. Finally, a case study with real -world data is used to show how the methodology works.

关键词： multi-objective problems multi-criteria Pareto pruning Borda count Decision making

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A multi-external archive-guided Henry Gas Solubility Optimization algorithm for solving multi-objective optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期109卷 104588-104588页

作者： Kahloul, Soumaia Zouache, Djaafar Brahmi, Boualem Got, Adel Univ Mohamed El Bachir El Ibrahimi Dept Math Bordj Bou Arreridj Algeria Univ Mohamed El Bachir El Ibrahimi Dept Comp Sci Bordj Bou Arreridj Algeria Univ Mohamed El Bachir El Ibrahimi Dept Operat Res Bordj Bou Arreridj Algeria Univ Sci & Technol Houari Boumediene LRIA Lab Algiers Algeria

This paper proposes a new multi-objective evolutionary algorithm by adapting the recent Henry Gas Solubility Optimization (HGSO) with multiple objectives. The proposed MOHGSO uses the Pareto dominance relation as means of comparison and integrates two types of archive, while an elite archive is used to store the Pareto solutions found over the evolutionary process, the other external-archives are used to store the local best solutions corresponding to each cluster. Moreover, efficient archiving and leader selection strategies based on the crowding distance computation are proposed to guide the population towards the true Pareto front. The performance of the MOHGSO algorithm is validated through an extensive comparison with three well-known algorithms on twelve test functions and four engineering design problems. The experiments of two widely used metrics in the field called IGD and Sp metrics show the ability of the proposed algorithm in achieving interesting results. Furthermore, the statistical results related to the Wilcoxon test indicate that the proposed algorithm outperforms significantly the selected methods for the above metrics at a 99% confidence level.

关键词： multi-objective problems Henry Gas Solubility Optimizer Pareto set External archive Crowding distance computation

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Neighborhood Learning Bacterial Foraging Optimization for Solving multi-objective problems

Neighborhood Learning Bacterial Foraging Optimization for So...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Niu, Ben Liu, Jing Chen, Jingsong Yi, Wenjie Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Shenzhen Polytech Sch Comp Engn Shenzhen 518055 Peoples R China

ISBN: (纸本)9783319410098;9783319410081

Based on the concept of neighborhood learning, this paper proposes a novel heuristic algorithm which is called Neighborhood Learning multi-objective Bacterial Foraging Optimization (NLMBFO) for solving multi-objective problems. This novel algorithm has two variants: NLMBFO-R and NLMBFO-S, using ring neighborhood topology and star neighborhood topology respectively. Learning from neighborhood bacteria accelerates the bacteria to approach the true Pareto front and enhances the diversity of optimal solutions. Experiments using several test problems and well-known algorithms test the capability of NLMBFOs. Numerical results illustrate that NLMBFO performs better than other compared algorithms in most cases.

关键词： Neighborhood learning Bacterial foraging optimization multi-objective problems

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An Archive-Based multi-objective Arithmetic Optimization Algorithm for Solving Industrial Engineering problems

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IEEE ACCESS 2022年 10卷 106673-106698页

作者： Khodadadi, Nima Abualigah, Laith El-Kenawy, El-Sayed M. Snasel, Vaclav Mirjalili, Seyedali Florida Int Univ Dept Civil & Environm Engn Miami FL 33199 USA Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Delta Univ Sci & Technol Fac Artificial Intelligence Mansoura 35712 Egypt Delta Higher Inst Engn & Technol DHIET Dept Commun & Elect Mansoura 35111 Egypt VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70032 Czech Republic Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide SA 5000 Australia Yonsei Univ Yonsei Frontier Lab YFL Seoul 03722 South Korea

This research proposes an Archive-based multi-objective Arithmetic Optimization Algorithm (MAOA) as an alternative to the recently established Arithmetic Optimization Algorithm (AOA) for multi-objective problems (MAOA). The original AOA approach was based on the distribution behavior of vital mathematical arithmetic operators, such as multiplication, division, subtraction, and addition. The idea of the archive is introduced in MAOA, and it may be used to find non-dominated Pareto optimum solutions. The proposed method is tested on seven benchmark functions, ten CEC-2020 mathematic functions, and eight restricted engineering design challenges to determine its suitability for solving real-world engineering difficulties. The experimental findings are compared to five multi-objective optimization methods (multi-objective Particle Swarm Optimization (MOPSO), multi-objective Slap Swarm Algorithm (MSSA), multi-objective Ant Lion Optimizer (MOALO), multi-objective Genetic Algorithm (NSGA2) and multi-objective Grey Wolf Optimizer (MOGWO) reported in the literature using multiple performance measures. The empirical results show that the proposed MAOA outperforms existing state-of-the-art multi-objective approaches and has a high convergence rate.

关键词： Optimization Arithmetic Heuristic algorithms Linear programming Particle swarm optimization Metaheuristics Search problems Pareto optimization Arithmetic optimization algorithm (AOA) archive-based multi-objective arithmetic optimization algorithm (MAOA) multi-objective problems engineering optimization

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multi-objective Pelican Optimization Algorithm for Engineering Design problems 19th

Multi-objective Pelican Optimization Algorithm for Engineeri...

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19th International Conference on Distributed Computing and Intelligent Technology (ICDCIT)

作者： Naidu, Y. Ramu Natl Inst Technol Andhra Pradesh Sch Sci Tadepalligudem Andhra Pradesh India

ISBN: (纸本)9783031248474;9783031248481

This work presents an efficient multi-objective version of the Pelican Optimization Algorithm (POA) which is recently proposed in the family of meta-heuristic algorithms. It is called a multi-objective Pelican Optimization Algorithm (MOPOA). From the literature, it is observed that the POA performed well on a set of unconstrained classical optimization problems as well as some engineering design problems. To extend its applicability to multi-objective engineering design models, the MOPOA has been proposed and applied for two engineering design models, four bar truss and speed reducer problems. The obtained results are compared with the literature and they proved that the MOPOA is an efficient and robust optimizer.

关键词： Pelican optimization algorithm multi-objective problems Engineering design problems

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Optimality for E- convex multi-objective programming problems

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JOURNAL OF INEQUALITIES AND APPLICATIONS 2015年第1期2015卷 1-18页

作者： Emam, Tarek Univ Hail Dept Math Fac Sci Hail Saudi Arabia

In this paper, we are interested in deriving the sufficient and necessary conditions for an optimal solution of special classes of programming problems. These classes involve generalized E-[0, 1] convex functions. The characterization of efficient solutions for E-[0, 1] convex multi-objective programming problems is obtained. Finally, sufficient and necessary conditions for a feasible solution to be an efficient or properly efficient solution are derived.

关键词： E-[0,1] convex functions optimal solutions multi-objective problems properly efficient solutions

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Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

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APPLIED INTELLIGENCE 2014年第2期41卷 551-562页

作者： Ferreira, Leonardo Anjoletto Costa Ribeiro, Carlos Henrique da Costa Bianchi, Reinaldo Augusto Univ Metodista Sao Paulo Sao Paulo Brazil Inst Tecnol Aeronaut Sao Paulo Brazil Ctr Univ FEI Sao Paulo Brazil

This article presents two new algorithms for finding the optimal solution of a multi-agent multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a heuristic function applied in standard Reinforcement Learning algorithms to simplify and speed up the learning process of an agent that learns in a multi-agent multi-objective environment. In order to verify performance of the proposed algorithms, we considered a predator-prey environment in which the learning agent plays the role of prey that must escape the pursuing predator while reaching for food in a fixed location. The results show that combining modularization and acceleration using a heuristics function indeed produced simplification and speeding up of the learning process in a complex problem when comparing with algorithms that do not make use of acceleration or modularization techniques, such as Q-Learning and Minimax-Q.

关键词： Reinforcement learning Heuristically accelerated reinforcement learning multi-agent systems multi-objective problems

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The H5N1 algorithm: a viral-inspired optimization for solving real-world engineering problems

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ENGINEERING COMPUTATIONS 2025年第3期42卷 1024-1096页

作者： Le, Thang Xuan Bui, Thanh Tien Tran, Hoa Ngoc Univ Minho ISISE Dept Civil Engn Guimaraes Portugal Univ Transport & Commun Hanoi Vietnam

PurposeIn recent years, the development of metaheuristic algorithms for solving optimization problems within a reasonable timeframe has garnered significant attention from the global scientific community. In this work, a new metaheuristic algorithm inspired by the inflection mechanism of the avian influenza virus H5N1 in poultry and humans, taking into account its mutation mechanism, called ***/methodology/approachThis algorithm aims to explore optimal solutions for optimization problems by simulating the adaptive behavior and evolutionary process of the H5N1 virus, thereby enhancing the algorithm's performance for all types of optimization problems. Additionally, a balanced stochastic probability mechanism derived from the infection probability is presented. Using this mechanism, the H5N1 algorithm can change its phrase, including exploitation and exploration phases. Two versions of H5N1, SH5N1 and MH5N1, are presented to solve single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs).FindingsThe performance of the algorithm is evaluated using a set of benchmark functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal to solve SOPs, ZDT functions and CEC2009 has been used to demonstrate its superiority over other recent algorithms. Finally, six optimization engineering problems have been tested. The results obtained indicate that the proposed algorithm outperformed ten algorithms in SOPs and seven algorithms in ***/valueThe experimental findings demonstrate the outstanding convergence of the H5N1 algorithm and its ability to generate solutions of superior quality.

关键词： Optimization algorithms Single-objective problems multi-objective problems Meta-heuristics Engineering problems

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Solving multi-objective problems using SPEA2 and Tabu Search

Solving Multi-Objective Problems using SPEA2 and Tabu Search

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Iranian Conference on Intelligent Systems (ICIS)

作者： Karimi, Fatemeh Lotfi, Shahriar Shahab Danesh Inst Higher Educ Dept Comp Engn Qom Iran Univ Tabriz Dept Comp Sci Tabriz Iran

ISBN: (纸本)9781479933501

Evolutionary Algorithms (EA's) are often well-suited for optimization problems involving several, often conflicting objectives. In this paper an improved hybrid method based on Strength Pareto Evolutionary Algorithm2 (SPEA2) and Tabu Search (TS) is proposed to handle the multi-objective optimization problems (MOPs). This method uses the exploration capacity of SPEA2 beside the power of TS in neighborhood research to find Pareto optimal solutions in different multi objective problems. To have a good distribution of solutions, proposed approach also uses an Improved Diversificator Tabu Search (IDTS) to find unexplored zones of Pareto front and achieve a comprehensive coverage. To test and compare this model with previous works, functions (ZDT1, ZDT2, and ZDT3) are selected. Experimental results show not only clear improvement of the proposed method, in contrast to the previous approaches, but good coverage and distribution of the Pareto front points have been achieved.

关键词： multi-objective problems Evolutionary Algorithms SPEA2 Tabu Search

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