Diversified Late Acceptance Search

Namazi, Majid; Sanderson, Conrad; Newton, M. A. Hakim; Polash, Md. Masbaul Alam; Sattar, Abdul

doi:10.1007/978-3-030-03991-2_29

Cited by 9 publications

(14 citation statements)

References 13 publications

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“…The focal distance TS algorithm, both by reference to a single solution x * and a set of solutions X * , can be employed in a natural way in conjunction with a variety of metaheuristics that include diversification processes, including iterated local search as described in [18,19], and the adaptive perturbation procedures of breakout local search in [20][21][22]. Another potentially fertile application of the approach can be to augment the thresholding methods of late acceptance hill climbing in [23] and diversified late acceptance search in [24]. Joining the focal distance TS approach with GRASP, particularly in the versions of GRASP that incorporate path relinking as in [25], and with the dynamic diversification strategy in [26], afford additional opportunities for future research.…”

Section: Discussionmentioning

confidence: 99%

Focal distance tabu search

Glover

2021

Sci. China Inf. Sci.

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Focal distance tabu search modifies a standard tabu search algorithm for binary optimization by augmenting a periodic diversification step that drives the search away from a current best (or elite) solution until the objective function deteriorates beyond a specified threshold or until attaining a lower bound on the distance from the originating solution. The new augmented algorithm combines the threshold and lower bound approaches by introducing an initial focal distance for the lower bound which is updated when the diversification step is completed. However, rather than terminating the diversification step at the customary completion point, focal distance tabu search (TS) retains the focal distance bound through additional search phases designed to improve the objective function, drawing on a strategy proposed with strategic oscillation. The algorithm realizes this strategy by partitioning the variables into two sets which are managed together with an abbreviated tabu search process. An advanced version of the approach drives the search away from a collection of solutions rather than a single originating solution, introducing the concept of a signature solution to guide the search. The method can be employed to augment a variety of other metaheuristic algorithms such as those using threshold procedures, late acceptance hill climbing, iterated local search, breakout local search, GRASP, and path relinking.

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Section: Discussionmentioning

confidence: 99%

Focal distance tabu search

Glover

2021

Sci. China Inf. Sci.

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“…Wu et al [44] developed a gamebased memetic algorithm for vertex cover of networks, where local improvement is based on an asynchronous updating best response rule of snowdrift game. For the critical node problem of Section IV, our local improvement procedure is based on a diversified late acceptance search algorithm [28].…”

Section: Local Improvementmentioning

confidence: 99%

“…2) Diversified late acceptance search: Diversified late acceptance search (DLAS) [28] is an iterative local search algorithm that is inspired by the late acceptance hill climbing (LAHC) algorithm [9]. Both DLAS and LAHC start their search from an initial solution and iteratively accepts or rejects candidate solutions until a given stopping condition is met.…”

Section: Variable Population Memetic Algorithm For Cnpmentioning

confidence: 99%

Variable Population Memetic Search: A Case Study on the Critical Node Problem

Zhou

Hao

et al. 2019

Preprint

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Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds.

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“…The late acceptance strategy delays the comparison, where a new candidate solution is compared with one of some pre-encountered solutions. Based on the late acceptance strategy, several effective HC-based algorithms have been proposed [34], [35]. However, as indicated by Namazi et al [35] and Zhou et al [28], they are generally time-consuming to achieve a good result.…”

mentioning

confidence: 99%

“…Based on the late acceptance strategy, several effective HC-based algorithms have been proposed [34], [35]. However, as indicated by Namazi et al [35] and Zhou et al [28], they are generally time-consuming to achieve a good result. To speed up the search, we propose a LADS, which effectively integrates the above incremental evaluation technique to evaluate a candidate solution in an incremental manner.…”

mentioning

confidence: 99%

Heuristic Search for Rank Aggregation with Application to Label Ranking

Zhou¹,

Hao²,

Li³

et al. 2022

Preprint

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Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of practical applications, however, it is a computationally challenging problem. In this paper, we propose an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and a late acceptance local search reinforced by an efficient incremental evaluation technique. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, which is an important machine learning task.

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Diversified Late Acceptance Search

Cited by 9 publications

References 13 publications

Focal distance tabu search

Focal distance tabu search

Variable Population Memetic Search: A Case Study on the Critical Node Problem

Heuristic Search for Rank Aggregation with Application to Label Ranking

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