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MOBRO: multi-objective battle royale optimizer

JOURNAL OF SUPERCOMPUTING 2024年第5期80卷 5979-6016页

作者： Alp, Sait Dehkharghani, Rahim Akan, Taymaz Bhuiyan, Mohammad A. N. Erzurum Tech Univ Dept Comp Engn Erzurum Turkiye Kadirhas Univ Dept Comp Engn Dept Management Informat Syst Istanbul Turkiye Louisiana State Univ Hlth Sci Ctr Dept Med Shreveport LA 71103 USA Istanbul Topkapi Univ Istanbul Turkiye

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.

关键词： Optimization Battle-royale-game-based optimization algorithms Battle royale optimization algorithm multi-objective problems

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MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing

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JOURNAL OF SUPERCOMPUTING 2024年第17期80卷 25004-25028页

作者： Ghasemi, Arezoo Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran

The fog computing model is a new computing model that has been proposed in recent years by increasing the number of requests sent to the cloud to reduce the delay and workload of the cloud computing model. In addition to its advantages, the fog computing model also has challenges, among which we can mention the issue of service placement in this computing model, which is very effective in the performance of the computing model. So far, many works have been presented to solve the problem of service deployment by considering different goals such as energy consumption, end-to-end delay, load balancing, resource efficiency, etc. Considering the importance of all the mentioned parameters, it is very important to provide a multi-objective method. In multi-objective problems, the method of evaluating the generated solutions is a separate challenge. Therefore, in this paper, a service placement method is presented by considering end-to-end delay criteria and energy consumption based on the modified Harris hawks algorithm to solve multi-objective problems. To increase accuracy, in the proposed method called multi-objective Harris hawks optimization, a multi-objective problem is modeled as several single-objective problems. The simulation results in CloudSim show that the proposed method has achieved better results than other algorithms in terms of reducing energy consumption, end-to-end delay, and network utilization.

关键词： Fog computing Service placement Harris hawks optimization multi-objective problems

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Model of a Two-Level Hierarchical System with Cooperative Behavior of Lower-Level Elements

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DOKLADY MATHEMATICS 2024年第SUPPL2期110卷 S346-S356页

作者： Gorelov, M. A. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

One of the possible generalizations to the case of many persons of the classical hierarchical Germeier game has been studied. It has been assumed that the lower-level players select one of the weakly efficient points. The maximal guaranteed result of the top-level player has been calculated. Two variants of formulation of the problems: games with and without feedback have been considered.

关键词： hierarchical games maximal guaranteed result multi-objective problems

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Improving multi-objective Evolutionary Optimization via Population Distribution Transfer and its Application 6

Improving Multi-objective Evolutionary Optimization via Popu...

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6th International Conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Xue, Tongxia Mao, Huiting Shi, Xuhua Ningbo Univ Fac Elect Engn & Comp Sci Ningbo Peoples R China

ISBN: (纸本)9798350377859;9798350377842

multi-objective evolutionary optimization is a class of challenging and complex optimization problems aimed at searching a set of optimal solutions in the face of multiple conflicting objectives. NSGA-III, which is an example of dominance-based algorithms, demonstrates significant capability but struggles with convergence as the number of objectives increases. Here, a multi-objective evolutionary optimization method, named OKTPC, is proposed, which is based on the distribution transfer of populations from single-objective evolutionary optimization. Firstly, the elite population distribution features of single objective problems (SOP) are extracted as knowledge information, and then, the population distribution features of SOP are periodically transferred to the multi-objective problems (MOP) to guide the evolutionary direction in the evolutionary *** prevent SOPs from falling into local optima and causing negative transfer, a SOP evolution strategy combined with a clustering algorithm is designed. Benchmark tests and distillation optimization experiment results demonstrate that the proposed OKTPC can effectively enhance the overall quality of solutions by significantly improving both their convergence speed and maintaining high diversity.

关键词： multi-objective problems knowledge transfer population distribution evolutionary algorithm

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Evolutionary Computing Approaches for Solving multi-objective and Many-objective Optimization problems: A Review 5

Evolutionary Computing Approaches for Solving Multi-Objectiv...

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5th International Conference on Computing, Communication Control and Automation (ICCUBEA)

作者： Shinde, Spurti Sachin Thangavelu, S. Jeyakumar, G. Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781728140421

In today world, there exists different algorithmic approaches in Computer Science to solve the complex realworld optimization problems around us. Based on the number of objectives they are classified as single-objective, multiobjective or many-objective optimization problems. Though many approaches are available for solving single-objective optimization problems, they are not directly scalable to solve multi-objective and Many-objective optimization problems. However, the Evolutionary Computing (EC) based approaches are becoming most popular and common to solve all the types of optimization problems, due to their simplicity and wide applicability. This paper presents the details of the Evolutionary Algorithms (EAs) to solve the optimization problem with the category of EAs for multi-objective and EAs for Many-objective optimization problems. Insight about the Hybridization of EAs is also added. The details about the algorithmic components required for designing EAs is also presented along with the Benchmarking problems and Performance Metrics available to validate the newly designed EAs.

关键词： Optimization problems multi-objective problems Many-objective problems Evolutionary Algorithm Hybridization

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Robust multi-objective Particle Swarm Optimization Algorithm Based on Hybrid Metric 13

Robust Multi-Objective Particle Swarm Optimization Algorithm...

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13th Asian Control Conference (ASCC)

作者： Zhou, Hao Huang, Yanting Han, Honggui Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9788993215236

In this paper, a robust multi-objective particle swarm optimization algorithm, based on hybrid metric (RMOPSO-HM), is proposed to depress the adverse effect of uncertainties on optimization performance. First, an adaptive sampling strategy is developed to dynamically adjust the sampling quantity of decision variables by evaluating their robust relevance. Unnecessary computation is reduced and sampling efficiency is improved by this strategy. Second, a sampling-based robustness evaluation metric is designed by incorporating both the preference robustness (PR) and the dominance robustness (DR). The hybrid metric facilitates a more comprehensive assessment of the robustness and avoids missing robust optimal solutions. Third, the robustness is embedded into the evolutionary mechanism by a weight preference operator and a new objective function is defined, which can tradeoff the optimality and the robustness. Finally, the hybrid metric and the improved evolutionary mechanism are applied to a parametric adaptive particle swarm optimization algorithm to guide the population evolution. Experimental results illustrate that the designed robust optimization algorithm maintains a superior optimization performance despite the impact of uncertainties.

关键词： robust optimization multi-objective problems particle swarm optimization

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An Initial Investigation of Data-Lean Transfer Evolutionary Optimization with Probabilistic Priors

An Initial Investigation of Data-Lean Transfer Evolutionary ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Lim, Ray Gupta, Abhishek Ong, Yew-Soon Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Agcy Sci Technol & Res Singapore Inst Mfg Technol Singapore Singapore Agcy Sci Technol & Res Singapore Singapore

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Transfer evolutionary optimization (TrEO) has emerged as a computational paradigm to leverage related problem-solving information from various source tasks to boost convergence rates in a target task. State-of-the-art TrEO algorithms have utilized a source-target similarity capture method with probabilistic priors that grants the ability to reduce negative transfers. A recent work makes use of an additional solution representation learning module to induce high ordinal correlation between source and target objective functions through source-to-target search space mappings, with the aim of promoting positive transfers between them. However, current implementations of this approach are found to be data-intensive - calling for all generated source data to be cached - leading to high storage costs in practice. As an alternative, this paper investigates the feasibility of a data-lean variant of the aforesaid approach, labeled as (1, G)-TrEO, in which only the first and final (Gth) generations of source data are used for solution representation learning and transfer. We conduct experimental analyses of (1, G)-TrEO using multi-objective benchmark functions as well as a practical example in vehicle crashworthiness design. Our results show that a simple data-lean transfer optimizer is able to achieve competitive performance. While this paper presents a first investigation of (1, G)-TrEO, we hope that the findings would inspire future forms of data-lean TrEO algorithms.

关键词： transfer evolutionary optimization solution representation learning data-lean knowledge transfer probabilistic model-based transfer multi-objective problems

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A Search Method Combining Average Distance of Particle Swarm and Crowding Distance of Particles 41

A Search Method Combining Average Distance of Particle Swarm...

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第41届中国控制会议

作者： Yu Ci Chen Peng Miao Rong School of Mechatronic Engineering and Automation Shanghai University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

It is popular to constantly improve existing optimization algorithms by embedding intelligent *** this paper,with particle swarm optimization algorithm(PSO) as the framework,the average distance of particle swarm is combined with the crowding distance of particles to provide conveniences for selecting the global optimal particle and deleting redundant particles in Pareto optimal set(PS).What's more,exponential weight and adaptive mutation are used to control the trade-off between global and local *** proposed method has been evaluated by inverted generation distance(IGD) and compared with several typical multi-objective optimization *** results show that the proposed method can improve the ability of searching for unknown solutions and also obtain Pareto front(PF) with good distribution on most of standard functions.

关键词： Average distance Crowding distance Particle swarm optimization multi-objective problems

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A competitive mechanism integrated multi-objective whale optimization algorithm with differential evolution q

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NEUROCOMPUTING 2021年 432卷 170-182页

作者： Zeng, Nianyin Song, Dandan Li, Han You, Yancheng Liu, Yurong Alsaadi, Fuad E. Xiamen Univ Sch Aerosp Engn Xiamen 361005 Fujian Peoples R China Yangzhou Univ Dept Math Yangzhou 225002 Jiangsu Peoples R China King Abdulaziz Univ Fac Engn Commun Syst & Networks CSN Res Grp Jeddah 21589 Saudi Arabia

In this paper, a competitive mechanism integrated whale optimization algorithm (CMWOA) is proposed to deal with multi-objective optimization problems. By introducing the novel competitive mechanism, a better leader can be generated for guiding the update of whale population, which benefits the convergence of the algorithm. It should also be highlighted that in the competitive mechanism, an improved calculation of crowding distance is adopted which substitutes traditional addition operation with multiplication operation, providing a more accurate depiction of population density. In addition, differential evolution (DE) is concatenated to diversify the population, and the key parameters of DE have been assigned different adjusting strategies to further enhance the overall performance. Proposed CMWOA is evaluated comprehensively on a series of benchmark functions with different shapes of true Pareto front. Results demonstrate that proposed CMWOA outperforms other three methods in most cases regarding several performance indicators. Particularly, influences of model parameters have also been discussed in detail. At last, proposed CMWOA is successfully applied to three real world problems, which further verifies the practicality of proposed algorithm. CO 2020 Elsevier B.V. All rights reserved.

关键词： multi-objective problems Whale optimization algorithm (WOA) Competitive mechanism Differential evolution (DE)

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Number of bins and maximum lateness minimization in two-dimensional bin packing

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期291卷 101-113页

作者： Arbib, Claudio Marinelli, Fabrizio Pizzuti, Andrea Univ Aquila Dipartimento Ingn Sci Informaz & Matemat Via Vetoio I-67010 Laquila Italy Univ Aquila Ctr Excellence DEWS Via Vetoio I-67010 Laquila Italy Univ Politecn Marche Dipartimento Ingn Informaz Via Brecce Blanche I-60131 Ancona Italy

In this work we address an orthogonal non-oriented two-dimensional bin packing problem where items are associated with due-dates. Two objectives are considered: minimize (i) the number of bins and (ii) the maximum lateness of the items. We discuss basic properties of non-dominated solutions and propose a sequential value correction heuristic that outperforms two benchmark algorithms specifically designed for this problem. We also extend the benchmark dataset for this problem with new and larger industrial instances obtained from a major manufacturer of cutting machines. Finally, we give some insights into the structure of Pareto-optimal sets in the classes of instances here considered. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Packing Scheduling Heuristics multi-objective problems

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