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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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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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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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Efficient linear combination method for multi-objective problems with convex polyhedral preference functions

Efficient linear combination method for multi-objective prob...

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IEEE International Conference on Industrial Technology

作者： Phruksaphanrat, B Ohsato, A Nagaoka Univ Technol Grad Sch Engn Nagaoka Niigata 9402188 Japan

ISBN: (纸本)0780376579

At present, the most commonly used method for multiobjective linear, programming (MOLP) is goal programming (GP) based methods but these methods do not always generate efficient solutions. Recently, an efficient GP-based method, which is called reference goal programming (RGP), has been proposed. However, it is limited to only a certain target point preference, which is too rigid. The more flexible preferences are preferred in many practical problems. In this research, an effective linear combination method for MOLP problems with convex polyhedral preference functions is proposed. The concept of the convex cone is used to formulate convex polyhedral preference functions and the existing reference point method (RPM) is integrated to ensure the efficiency of the solution of the problem. The formulated model can be solved by existing linear programming solvers and can find the satisfactory efficient solution. The convex polyhedral function enriches the existing preferences for efficient methods and increases the flexibility in designing preferences for decision makers. For some situations, it is difficult for the decision maker to state the certain aspiration level for each objective function. Fuzzy goals, which can be considered as convex polyhedral preference functions, can be used to represent aspiration levels with respect to linguistic terms.

关键词： linear combination multi-objective problems convex polyhedral functions reference point method fuzzy goals and efficient solutions

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Correlative Particle Swarm Optimization for multi-objective problems

Correlative Particle Swarm Optimization for Multi-objective ...

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2nd International Conference on Swarm Intelligence (ICSI)

作者： Shen, Yuanxia Wang, Guoyin Liu, Qun Chongqing Univ Posts & Telecommun Inst Comp Sci & Technol Chongqing 400065 Peoples R China

ISBN: (纸本)9783642215230

Particle swarm optimization (PSO) has been applied to multi-objective problems. However, PSO may easily get trapped in the local optima when solving complex problems. In order to improve convergence and diversity of solutions, a correlative particle swarm optimization (CPSO) with disturbance operation is proposed, named MO-CPSO, for dealing with multi-objective problems. MO-CPSO adopts the correlative processing strategy to maintain population diversity, and introduces a disturbance operation to the non-dominated particles for improving convergence accuracy of solutions. Experiments were conducted on multi-objective benchmark problems. The experimental results showed that MO-CPSO operates better in convergence metric and diversity metric than three other related works.

关键词： multi-objective problems Correlative particle swarm optimization Population diversity

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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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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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Jacobian Matrix Singularity Based Pareto Front Identification for multi-objective problems

Jacobian Matrix Singularity Based Pareto Front Identificatio...

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American Control Conference

作者： Brandon Brown Tarunraj Singh Rahul Rai Department of Mechanical and Aerospace Engineering State University of New York at Buffalo Buffalo NY 14260

ISBN: (纸本)9781479901777

This paper presents a new structured method to effectively determine the complete boundary, including the Pareto frontier, of a multi-objective optimization problem. The proposed technique identifies the boundary in the cost space by systematically searching the design parameter space for points which make the Jacobian matrix of the cost vector singular. This corresponds to the identifying a manifold in parameter space which results in a reduced dimensional manifold in the cost space. Since the boundary of the cost space implies a reduced dimensional manifold, a systematic approach is now available for exact identification of the boundary in the cost space. The efficacy of the proposed method is demonstrated on one optimization and one optimal control problem, in this paper.

关键词： multi-objective problems Pareto frontier cost space

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Evolving dynamic multi-objective optimization problems with objective replacement

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ARTIFICIAL INTELLIGENCE REVIEW 2005年第3期23卷 267-293页

作者： Guan, SU Chen, Q Mo, WT Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore

This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.

关键词： multi-objective genetic algorithms multi-objective problems multi-objective optimization non-stationary environment

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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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