Multiobjective Optimization for Multimode Transportation Problems

Lemarchand, Laurent; Massé, Damien; Rebreyend, Pascal; Håkansson, Johan

doi:10.1155/2018/8720643

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike single-objective optimization (SOO), the purpose of MOO is to find a set of good solutions [55] representing trade-offs between objectives since a single solution optimizing all of the objectives simultaneously rarely exists [42]. A good solution is often defined by the dominance concept (see Section 2.2.2).…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

et al. 2021

Self Cite

View full text Add to dashboard Cite

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

show abstract

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar problems were solved with multi-objective optimization. Having mathematical dependences for several output functions, it is possible to solve the decision-making problem as a multi-objective optimization problem [12]. There are a fairly large number of scientific results in the field of multipurpose optimization [13][14][15].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of evolutionary search algorithms with binary choice relations when making decisions for pellet tubular heaters

Irodov¹,

Shaptala²,

Dudkin³

et al. 2021

EEJET

View full text Add to dashboard Cite

A study was carried out and the optimization process was carried out for one of the types of equipment for autonomous heat supply using renewable resources – a tubular pellet heater. The research is expedient, since there is no mathematical model of the unit operation for the pellet combustion unit, there is only a set of experimental results indicating the inconsistency of the criteria presented to it. As a result of the research, new algorithms have been obtained: firstly, an algorithm for selecting (multi-criteria optimization) the operating mode of the unit for burning pellets of tubular heaters, and secondly, algorithms for choosing, according to several criteria, the parameters of the heat exchange unit of a tubular heater with a screen. A set of algorithms for multicriteria optimization with binary selection ratios has been developed for tubular pellet heaters in full, including a pellet combustion unit and a heat exchange unit. Selection functions have been defined for a pellet combustion unit using dimensionless complexes based on experimental results. For a block of a tubular heat exchanger with a screen, a selection function is built taking into account the criteria of functioning and a mathematical model of the heater in the form of a system of nonlinear ordinary differential equations. The practical significance of the algorithm for selecting the operating mode for the pellet combustion unit lies in the possibility of obtaining the most preferable (optimal, taking into account many criteria) parameters in the entire range of permissible parameters, and not only among the experiments carried out. The practical significance of optimization algorithms for a heat exchange unit lies in the ability to select specific parameter values during design – the thermal power of the heater, air flow, the length of the tubular part and the screen, their diameters, taking into account several selection criteria.

show abstract

“…Evolutionary and genetic algorithms for solution search problems are being intensively developed. For example, in [3][4][5], they are applied to solve multiobjective optimization problems. In evolutionary algorithms, in solution of these problems, optimization of real functions rather than binary choice relations is considered.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization at Evolutionary Search with Binary Choice Relations

2020

View full text Add to dashboard Cite

A multiobjective optimization problem is considered, in which binary choice relations are used instead of optimized functions. To solve this problem, it is proposed to use an evolutionary random search algorithm, in which the function of choice in the form of a lock is used instead of the choice function in the form of a preference,. The convergence of the proposed evolutionary algorithms is analyzed, and sufficient conditions for convergence are formulated. The results of the proposed evolutionary search are compared with the results of well-known evolutionary algorithms for one test problem.

show abstract

Multiobjective Optimization for Multimode Transportation Problems

Cited by 6 publications

References 28 publications

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Development of evolutionary search algorithms with binary choice relations when making decisions for pellet tubular heaters

Multiobjective Optimization at Evolutionary Search with Binary Choice Relations

Contact Info

Product

Resources

About