Hyperheuristics Based on Parametrized Metaheuristic Schemes

Cutillas-Lozano, José-Matías; Giménez, Domingo; Almeida, Francisco

doi:10.1145/2739480.2754641

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…This does not guarantee the best performance of the algorithm. Moreover, as searching for the parameters is very time-consuming, one could consider the parameterized metaheuristic as presented in [52].…”

Section: Discussionmentioning

confidence: 99%

Hybrid Heuristic for Vehicle Routing Problem with Time Windows and Compatibility Constraints in Home Healthcare System

Saksuriya

Likasiri

2022

Applied Sciences

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This work involves a heuristic for solving vehicle routing problems with time windows (VRPTW) with general compatibility-matching between customer/patient and server/caretaker constraints to capture the nature of systems such as caretakers’ home visiting systems or home healthcare (HHC) systems. Since any variation of VRPTW is more complicated than regular VRP, a specific, custom-made heuristic is needed to solve the problem. The heuristic proposed in this work is an efficient hybrid of a novice Local Search (LS), Ruin and Recreate procedure (R&R) and Particle Swarm Optimization (PSO). The proposed LS acts as the initial solution finder as well as the engine for finding a feasible/local optimum. While PSO helps in moving from current best solution to the next best solution, the R&R part allows the solution to be over-optimized and LS moves the solution back on the feasible side. To test our heuristic, we solved 56 benchmark instances of 25, 50, and 100 customers and found that our heuristics can find 52, 21, and 18 optimal cases, respectively. To further investigate the proficiency of our heuristic, we modified the benchmark instances to include compatibility constraints. The results show that our heuristic can reach the optimal solutions in 5 out of 56 instances.

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Section: Discussionmentioning

confidence: 99%

Hybrid Heuristic for Vehicle Routing Problem with Time Windows and Compatibility Constraints in Home Healthcare System

Saksuriya

Likasiri

2022

Applied Sciences

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“…The problem of finding the optimum VAR model for a given series is an optimization problem whose solution can be approached through metaheuristics. The application of parameterized metaheuristic schemes has proved to be a practical approach for the determination of satisfactory metaheuristics for several problems [9,10]. Therefore, a flexible metaheuristic scheme has been used hybridizing it from a set of metaheuristics, both Local Search and population-based methods.…”

mentioning

confidence: 99%

“…The searching task of the most appropriate set of values for these parameters is a challenge in itself beyond the scope of this paper. One possibility to address it is by a hyperheuristic approach [10], where this set of values can be selected so that it is suitable for the set of problems on which it will be applied, avoiding the dependence of each specific problem. A hyperheuristic can be implemented on top of the parametrized metaheuristic schema, searching within the space of metaheuristics determined by the values of the metaheuristic parameters.…”

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confidence: 99%

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

et al. 2020

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In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.

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Optimizing a parameterized message-passing metaheuristic scheme on a heterogeneous cluster

Cutillas-Lozano

Giménez

2016

Soft Comput

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Hyperheuristics Based on Parametrized Metaheuristic Schemes

Cited by 11 publications

References 15 publications

Hybrid Heuristic for Vehicle Routing Problem with Time Windows and Compatibility Constraints in Home Healthcare System

Hybrid Heuristic for Vehicle Routing Problem with Time Windows and Compatibility Constraints in Home Healthcare System

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

Optimizing a parameterized message-passing metaheuristic scheme on a heterogeneous cluster

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