Boosting an evolution strategy with a preprocessing step: application to group scheduling problem in directional sensor networks

Srivastava, Gaurav; Singh, Alok

doi:10.1007/s10489-018-1252-9

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…ES has proven to be effective in addressing a wide range of optimization problems i.e. Distributed blocking FSSP 49 , Image segmentation 50 , Permutation FSSP 51 , 52 , Blocking FSSP 53 , Multiple traveling salesman problem 54 , Robust Permutation FSSP 55 , Team orienting problem 56 , Open vehicle routing 57 , Group scheduling problem 58 , Assembly line balancing 59 . Keeping in view the successful results obtained by using ES for various optimization problems, in this research work ES is designed and applied to NWFSSP to minimize the makespan.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

Khurshid,

Maqsood,

Khurshid

et al. 2024

Sci Rep

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This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.

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Section: Literature Reviewmentioning

confidence: 99%

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

Khurshid,

Maqsood,

Khurshid

et al. 2024

Sci Rep

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“…Shahhoseini and Torkzadeh [268] proposed a multi-constraints QoS routing algorithm. He et al [269] analyzed the data characteristics of wireless sensor network (WSN), and proposed a method for fault diagnosis of WSN based on a belief rule base (BRB) model.Srivastava and Singh [270] used ES for solving the the total rotation minimization problem (TRMP) in directional sensor networks. Srivastava et al [271] presented an ES method for solving the Cover scheduling problem in wireless sensor networks (WSN-CSP).…”

Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹

2021

Preprint

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<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

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“…Nissen and Gold [247] used ESs for designing a survivable network while taking economics and reliability into consideration. He et al [248] analyzed the data characteristics of wireless sensor network (WSN), and proposed a method for fault diagnosis of WSN based on a belief rule base (BRB) model which is optimized using CMA-ES.Srivastava and Singh [249] used ESs for solving the the total rotation minimization problem (TRMP) in directional sensor networks. Srivastava et al [250] presented an ESs method for solving the Cover scheduling problem in wireless sensor networks (WSN-CSP).…”

Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

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<div>Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.</div><div>To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. </div><div>After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. </div><div>Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. </div><div>Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.</div>

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Boosting an evolution strategy with a preprocessing step: application to group scheduling problem in directional sensor networks

Cited by 12 publications

References 34 publications

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

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