A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem

Sánchez-Díaz, Xavier; Ortíz-Bayliss, José Carlos; Amaya, Iván; Cruz–Duarte, Jorge M.; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

doi:10.1109/cec48606.2020.9185671

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…We divided them into three groups: SV perturbation, PF perturbation, and selection heuristics as aforementioned. The SV perturbators were designed conforming several works reported in the literature [32,33]. They were extracted from ten well-known metaheuristics: Random Search [34], Simulated Annelaing [35], Genetic Algorithm [36], Cuckoo Search [37], Differential Evolution [38], Particle Swarm Optimisation [39], Firefly Algorithm [40], Stochastic Spiral Optimisaiton Algorithm [41], Central Force Optimisation [42], and Gravitational Search Algorithm [43].…”

Section: Methodsmentioning

confidence: 99%

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

et al. 2021

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Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.

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Section: Methodsmentioning

confidence: 99%

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

et al. 2021

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“…The best hyper-heuristic constructed during the simulation can now be applied to unseen instances. The studies in [51,52] designed a feature-independent hyper-heuristic through an evolutionary algorithm to solve the 0/1 knapsack problem. Similar to the approach in the previous study [50], a set of training instances were used to construct viable hyperheuristics that were superior to the individual LLHs of the problem investigated.…”

Section: Related Studiesmentioning

confidence: 99%

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

2022

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Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms.

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“…Some not infrequent approaches found in the literature include the repetition of each element in the sequence, restarting the whole arrangement, and even mirroring the sequence [3]. Sánchez-Díaz et al already analyzed the behavior of the first approach [54] and so, in this work, we focus on the other two. The authors calculated how many times the whole sequence required to be repeated for making all the decisions for a single instance.…”

Section: B Hyper-heuristics (Hhs)mentioning

confidence: 99%

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

et al. 2021

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Combinatorial optimization problems (COPs) are paramount for real-life problems with discrete variables. Even though the number of combinations is finite, some problems exhibit exponential growth, rendering exact approaches unfeasible. So, approximate methods, such as heuristics, are customary for making fast decisions. Despite their small computational cost, each heuristic specializes in specific kinds of problems. Hence, other approaches have appeared for merging their strengths. One of these approaches is commonly known as selection hyper-heuristics. However, a trained hyper-heuristic usually carries the following problem: it provides scarce information about its sensitivity. This makes it more difficult to estimate the effect of changing a parameter. Illumination algorithms seek to improve this issue by focusing on exploration rather than on exploitation while preserving information about the best solutions with different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. Therefore, in this work, we test the feasibility of using an illumination algorithm, the MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since it has been successfully applied to a different COP, and by linking it with hyper-heuristics, we may exploit upon its benefits. So, we may achieve a hyper-heuristic that represents the best combination of heuristics. Simultaneously, we can gain information about the expected performance of diverse hyperheuristics, e.g., those that begin with a different heuristic. Our approach operates by creating a multidimensional map, where each design variable represents the application of a given heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. To test our approach, we consider 1500 instances that include easy and hard instances, analyzed under different scenarios. We also include limit instances that are neither easy nor hard. Our resulting data supports the feasibility of the proposed approach. We show that this kind of hyper-heuristic can perform toe-to-toe with a synthetic oracle. Besides, under some conditions, the former may even outperform the latter. This represents an outstanding result, since such an oracle can only be obtained by a brute-force approach, and because it demonstrates that merging both approaches (ME and hyper-heuristics) is a path worth pursuing. We also present how each parameter affects the model performance, concluding that some parameters can be critical while others are virtually irrelevant. This is also a relevant result, since it serves as the groundwork for future works so that they can focus on exploiting the most relevant parameters.

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A Preliminary Study on Feature-independent Hyper-heuristics for the 0/1 Knapsack Problem

Cited by 5 publications

References 21 publications

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

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