A novel method for solving min-max problems by using a modified particle swarm optimization

Masuda, Kazuaki; Kurihara, Kenzo; Aiyoshi, Eitaro

doi:10.1109/icsmc.2011.6083984

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Second, the RBF models also enable us to devise an intelligent way using the adaptive optimization strategy to solve the anti-optimization problem in which only promising region of the random parameter space is of interest. This overcomes the drawbacks of using either metaheuristic techniques [40,41] or the scenario approach [23] for solving the anti-optimization problem.…”

Section: Discussionmentioning

confidence: 99%

A random search for discrete robust design optimization of linear-elastic steel frames under interval parametric uncertainty

Ohsaki

2021

Computers & Structures

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Section: Discussionmentioning

confidence: 99%

A random search for discrete robust design optimization of linear-elastic steel frames under interval parametric uncertainty

Ohsaki

2021

Computers & Structures

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“…A worst case analysis can be approached by applying standard global metaheuristics to both the inner maximisation and the outer min. In a co-evolutionary approach inner and outer populations evolve separately, but the fitness of individuals in the outer min is determined by individuals in an inner maximisation, see [Her99,SK02,Jen04,CSZ09,MKA11]. However a completely brute force approach using full inner maximisation searches to inform the outer minimisation involves large numbers of function evaluations (model runs), see [MWPL16].…”

Section: State Of the Art 21 Literature Reviewmentioning

confidence: 99%

“…However a completely brute force approach using full inner maximisation searches to inform the outer minimisation involves large numbers of function evaluations (model runs), see [MWPL16]. Additional simplifications or assumptions are required to reduce the number of function evaluations in a co-evolutionary approach, see [CSZ09,MKA11].…”

Section: State Of the Art 21 Literature Reviewmentioning

confidence: 99%

Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty

Hughes¹,

Goerigk²,

Dokka³

2020

Preprint

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We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible.Problems of this type exhibit another maximisation layer to find the worst case solution within the minimisation level of finding a robust solution, which makes them harder to solve than classic global optimisation problems. So far, only few methods have been provided that can be applied to black-box problems with implementation uncertainty. We improve upon existing techniques by introducing a novel particle swarm based framework which adapts elements of previous approaches, combining them with new features in order to generate more effective techniques. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.

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“…A method for solving problem P3' using PSO is proposed in [23]. The main idea is to approximate the minimization and maximization function of the min-max problem with a finite number of search points, and obtain one solution of ''min − max = max − min'' problem by finding the minimum value of the maximization function and the maximum value of the minimization function.…”

Section: B Resource Allocationmentioning

confidence: 99%

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

et al. 2019

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As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources' allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms. INDEX TERMS Edge computing, multiple business, power Internet of Things, service delay, workload allocation.

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A novel method for solving min-max problems by using a modified particle swarm optimization

Cited by 7 publications

References 16 publications

A random search for discrete robust design optimization of linear-elastic steel frames under interval parametric uncertainty

A random search for discrete robust design optimization of linear-elastic steel frames under interval parametric uncertainty

Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty

Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things

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