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many objective optimization algorithm based on mixed species particle flocking

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第10期14卷 13251-13267页

作者： Anand, H. S. Saritha, R. Chandra, S. S. Vinod Muthoot Inst Technol & Sci Dept Comp Sci & Engn Kochi Kerala India Coll Engn Trivandrum Dept Comp Sci & Engn Thiruvananthapuram Kerala India Univ Kerala Dept Comp Sci Trivandrum Kerala India

A methodology inspired by the combined flocking nature of mixed species of birds is proposed in this paper. Mixed species flock with the intention to increase feeding efficiency or to defend predator attacks. Specific particle orientation in the entire population is controlled by particle's previous best location, position of the best species member and a globally best particle position. Non dominated solution set of the multiple objectives targeted by each species is acquired by tracking the trajectory of the globally best particle during current course of action. The proposed method provides adaptive population strategy by giving robust performance on divergent decision and objective space. Particle depletion and augmentation procedure increases the productivity of population to direct the solution set move closer to true pareto front with maximum spread and diversity. Visualisation of the non dominating set is provided using a solution ranking approach supported by a heap tree. It also acts as an ordered repository of partial solutions along with a quality ranking parameter. Performance of the method is evaluated over a standard set of multi objective test problems from the latest many objective function(MaF) test suit. Quantitative and qualitative assessment of the proposed model with state of the art many objective optimization algorithms in the field of natural computing depicts its endurance on scalable problems of different dynamics without compromising the computational complexity, but with finer storage alternative.

关键词： many objective optimization Mixed species flocking Particle swarm optimization Pareto front

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A coevolutionary approach to many objective optimization based on a novel ranking method

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INTELLIGENT DATA ANALYSIS 2016年第1期20卷 129-151页

作者： Rad, Mohammad Abbasi Hamzeh, Ali Shiraz Univ CSE & IT Dept Shiraz Iran

Ranking solutions of the population in an evolutionary algorithm that solves a many objective optimization problem is a challenging task which has been vastly studied in recent years. Loss in the hypervolume of the population when a solution is omitted could be a good measure for ranking solutions but calculating this value for high dimensional problems is not tractable. In the selection operator of evolutionary algorithms, the actual hypervolume values are not important, with only a relative knowledge of these values we can select best solutions. In this paper we have proposed a novel method for approximating the ranking induced by the hypervolume indicator. This method is compared to the similar methods and its efficiency and performance is proved via proper experiments. The method is tested using benchmark test problems from the Walking Fish Group problem family which are scalable both in number of variables and objectives.

关键词： Coevolution hypervolume approximation many objective optimization

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A Steady State Decomposition Based Quantum Genetic Algorithm for many objective optimization

A Steady State Decomposition Based Quantum Genetic Algorithm...

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IEEE Congress on Evolutionary Computation

作者： Ray, Tapabrata Asafuddoula, Md Isaacs, Amitay Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia IBM Australia Canberra ACT Australia

ISBN: (纸本)9781479904549;9781479904532

many objective optimization refers to the class of optimization problems involving four or more objectives. Optimal solutions of such problems lie on a hyper surface (the Pareto front) and the dimensionality of the hyper-surface is dependent on the number of objectives in conflict. Identifying well converged and well spread set of solutions spanning the hyper-surface is a non-trivial problem. In this paper we introduce a novel decomposition based steady state quantum genetic algorithm, wherein systematic sampling is used to generate the reference directions and a small population of quantum individuals (solutions with variables represented as Q-bits) is evolved using a simple variation operator. A solution represented using Q-bits has the ability to probabilistically represent a number of solutions defined through observation. We exploit the benefits of quantum representation within a steady state evolution scheme and illustrate the behavior of the algorithm using discrete formulations of the unconstrained DTLZ2 test problem involving 2, 3, 5, and 8 objectives. In order to illustrate the behavior for constrained optimization problems, we investigate the behavior of the algorithm using the water resource optimization problem involving 5 objectives. A quantum population of 5 individuals has been used to solve all the above problems. Preliminary results on the effects of the size of quantum population and the fidelity of representation are presented in this paper. Finally, a number of possible directions are suggested to further improve the performance of the algorithm.

关键词： many objective optimization Quantum Genetic Algorithm Decomposition Based Approach

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A many objective chimp optimization algorithm to de-cluster earthquake catalogs in space time domain

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SOFT COMPUTING 2024年第6期28卷 5287-5320页

作者： Sharma, Ashish Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Jaipur Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

Earthquakes are complex phenomena that generate various forms of clusters that are highly correlated in the space and time domains. To study the dynamic properties of seismicity and estimate risk, the earthquake catalog needs to be separated into clustered and background events. Here, seismicity de-clustering is formulated as a binary many-objective optimization problem. A swarm-based many-objective chimp optimization algorithm (MaOChOA) is proposed to segregate the earthquake catalog into aftershocks and background events. The reference point-based leader selection strategy is adopted to update the solution. The proposed MaOChOA is evaluated on many objective benchmark test functions. The results are compared with existing many-objective techniques using performance metrics such as generational distance, inverse generational distance, spacing metric, hyper-volume distance, and runtime. Further, de-clustering the earthquake catalog is performed using a binary version of many-objective Chimp optimization algorithms (BMaChOA), where the sigmoid function is used in the position update mechanism. The BMaChOA is applied to 32-year historical earthquake catalogs of the Japan, California, Indonesia, and Himalayan regions. The potential of the proposed algorithm is reported by comparing it to five benchmark de-clustering techniques. The results are validated using the epicenter plot, inter-event time Vs. inter-event distance plot, cumulative plot, lambda-plot, and statistical parameters such as the m-Morisita index, coefficient of variance, average nearest neighbor, and nearest neighbor distance.

关键词： many objective optimization Chimp optimization algorithm Reference point Seismicity de-clustering Coefficient of variance

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many-objective optimization for Knapsack Problems Using Correlation-Based Weighted Sum Approach

Many-Objective Optimization for Knapsack Problems Using Corr...

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5th International Conference on Evolutionary Multi-Criterion optimization

作者： Murata, Tadahiko Taki, Akinori Kansai Univ Dept Informat 2-1-1 Ryozenji Osaka 5691073 Japan Kansai Univ Inst Socionetwork Strategies Policy Grid Comp Lab Osaka 5648680 Japan Kansai Univ Grad Sch Informat Osaka 5648680 Japan

ISBN: (纸本)9783642010194

In this paper, we examine the effectiveness of an EMO (Evolutionary Multi-criterion optimization) algorithm using a correlation based weighted sum for many objective optimization problems. Recently many EMO algorithms are proposed for various multi-objective problems. However, it is known that the convergence performance to the Pareto-frontier becomes weak in approaches using archives for non-dominated solutions since the size of archives becomes large as the number of objectives becomes large. In this paper, we show the effectiveness of using a correlation information between objectives to construct groups of objectives. Our simulation results show that while an archive-based approach, such as NSGA-II, produces a set of non-dominated solutions with better objective values in each objective, the correlation-based weighted sum approach can produce better compromise solutions that has averagely better objective values in every objective.

关键词： many objective optimization problem correlation weighted sum approach archive-based approach

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Quantum particle swarm algorithm for many-objective optimization problem 11

Quantum particle swarm algorithm for Many-objective optimiza...

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11th World Congress on Intelligent Control and Automation

作者： Xia Changhong Zhang Yong Gong Dunwei Sun Xiaoyan China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781479958252

many-objective optimization problems widely exist in real world. However, there is lack of effective solutions to solve this problem because they contain more than three conflicting objectives. Based on quantum particle swarm optimization algorithm, this paper presents an efficient many-objective particle swarm optimization algorithm. In this algorithm, an improved quantum update method is introduced to update the particles' positions, a selection strategy based on the global difference order is proposed to update the global best position of particle, and a congestion sorting strategies is applied to update the external repository. By optimizing ZDT and DTLZ series functions, and comparing with representative algorithms such as TV-MOPSO, results indicate that the proposed algorithm is effective for solving many-objective optimization problems.

关键词： particle swarm optimization many objective optimization Global difference order external repository

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Reference-lines steered guide assignment and update for pareto-based many-objective particle swarm optimization

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EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 89-114页

作者： Sharma, Deepak Agarwal, Devang Kumar, Santosh Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 Assam India

The reference-lines-framework has been successfully used for developing efficient many-objective evolutionary algorithms. In this paper, the concepts and methodologies of such evolutionary algorithms are adapted in the parlance of multi-objective particle swarm optimization (MOPSO) for addressing the challenges of assigning and updating the global and local guides. The proposed algorithm, which is referred to as RMaOPSO, is developed via five modules using the framework so that a diverse set of guides can be selected to steer the search of MOPSO toward the Pareto-optimal front. The modules include global guide assignment, local and global guide update, line assignment to the guides and swarm, and evolutionary search for global guides. The proposed algorithm is tested on DTLZ and WFG test instances of 3-, 5-, 8-, 10- and 15- objectives. Results obtained from RMaOPSO show its efficacy over six multi-objective evolutionary and MOPSO algorithms from the literature.

关键词： MOPSO Guide assignment Guide update Reference lines many objective optimization

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Multi-objective and many objective design of plastic injection molding process

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第9-12期102卷 3165-3180页

作者： Alvarado-Iniesta, Alejandro Cuate, Oliver Schutze, Oliver Univ Autonoma Ciudad Juarez Dept Ind & Mfg Engn Ciudad Juarez Chihuahua Mexico Cinvestav IPN Dept Comp Sci Mexico City DF Mexico UAM Cuajimalpa Dept Appl Math & Syst Dr Rodolfo Quintero Chair Mexico City DF Mexico

Plastic injection molding is one of the most used manufacturing processes capable of producing flexible and economical parts at a large scale. Since this is a highly complex process, it is a natural consequence that there are many conflicting objectives that are worth considering in the design of such process. Problems where more than three objectives are being considered at the same time are termed many objective problems (MaOPs) in literature. Unlike for multi-objective problems (MOPs, problems with two or three objectives), there is no consensus of how to find ideal solutions for general MaOPs. In this paper, the multi-objective and many objective design of a plastic injection molding process is addressed. To accomplish this task, the two main contributions of this work are as follows: first, a new optimization model that contains up to seven objectives is proposed. That is, for the first time, it is considered the many objective design of a plastic injection process. Second, the usefulness of the Pareto Explorer, a global/local exploration tool for MaOPs, in the current context is demonstrated. For this, the complete seven-objective optimization problem on several selected scenarios related to the hypothetical decision making of a plastic gear is considered.

关键词： Plastic injection molding Multi-objective optimization many objective optimization Decision making

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Can modern multi-objective evolutionary algorithms discover high-dimensional financial risk portfolio tradeoffs for snow-dominated water-energy systems?

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ADVANCES IN WATER RESOURCES 2020年 145卷 103718-103718页

作者： Gupta, Rohini S. Hamilton, Andrew L. Reed, Patrick M. Characklis, Gregory W. Cornell Univ Dept Civil & Environm Engn Ithaca NY 14853 USA Univ North Carolina Chapel Hill Gillings Sch Global Publ Hlth Dept Environm Sci & Engn Chapel Hill NC USA Univ North Carolina Chapel Hill UNC Inst Environm Ctr Financial Risk Environm Syst Gillings Sch Global Publ Hlth Chapel Hill NC USA

Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and timing. This study explores the Hetchy Hetchy Power System as a representative example from the broader class of financial risk management problems that hold promise in helping utilities such as SFPUC to understand the tradeoffs across portfolios of risk mitigation instruments given uncertainties in snowmelt dynamics. An evolution-ary multi-objective direct policy search (EMODPS) framework is implemented to identify time adaptive stochastic rules that map utility state information and exogenous inputs to optimal annual financial decisions. The resulting financial risk mitigation portfolio planning problem is mathematically difficult due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. These features add to the difficulty of the problem by yielding a Pareto front of solutions that has a highly disjoint and complex geometry. In this study, we contribute a diagnostic assessment of state-of-the-art multi-objective evolutionary algorithms' (MOEAs') abilities to support a DPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided Evolutionary Algorithm (RVEA), and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The MOEAs are evaluated to characterize their controllability (ease-of-use), reliability (probability of success), efficiency (minimizing model evaluations), and effectiveness (high quality tradeoff representations). Our results show that newer decomposition, reference point, and reference vector algorithms are highly sensitive to their parameterizations (difficult

关键词： Financial risk management Direct policy search many objective optimization Evolutionary algorithms Hydropower Algorithm benchmarking

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Nautilus: An Interactive Plugand-Play Search-Based Software Engineering Framework

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IEEE SOFTWARE 2021年第5期38卷 73-82页

作者： Ferreira, Thiago Nascimento Vergilio, Silvia Regina Kessentini, Marouane Univ Fed Parana Curitiba Parana Brazil Univ Fed Parana Comp Sci Dept Software Engn SE BR-81531980 Curitiba Parana Brazil Univ Michigan Ann Arbor MI 48109 USA

Nautilus Framework allows practitioners to develop and experiment with several multi- and many-objective evolutionary algorithms-guided (or not) by human participation-in a few steps with a minimum required background... 详细信息

关键词： Evolutionary Computation Search Problems Software Engineering Nautilus Framework many objective Evolutionary Algorithms Search Based Algorithms Human Participation Interactive Plug And Play Search Based Software Engineering Framework optimization Software Algorithms Search Problems Software Engineering Linear Programming Encoding Visualization Preference And Search Based Software Engineering many objective optimization Plugin And Play Framework

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