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3 Recommendations

Mohamed B Trabia

University of Nevada, Las Vegas

Hi Askan

There is no unique answer! Perhaps you should ask yourself these questions:

1. Is the problem highly nonlinear?

2. Does it have lots of discontinuities?

3. Do you have mathematical representation of the problem?

4. Is it computationally intensive?

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2 Recommendations

Ashkan Memari

Central Queensland University

Abdelkader Sbihi:

Hi

Thanks for reply. However I am looking for the best fitted local search with NSGA-II. Some of the pervious metaheuristic such as Simulated Annealing is widely applied so far. I am looking for recently developed metaheuristic with good ability to be fitted with NSGA-II

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1 Recommendation

Ashkan Memari

Central Queensland University

Mohamed B Trabia:

Hi

The problem is a discrete linear bi-objective optimization model. I already solve the problem using NSGA-II. In order to enhance the performance, I would like to add a local search. Now the problem is 2 folded:

1. Which currently metaheuristic can be added as a local search? Why?

I don't want to apply the pervious methods like SA or TS.

2. How to add this local search to NSGA-II  ? 

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1 Recommendation

Mohamed B Trabia

University of Nevada, Las Vegas

Hi Askan

I suspect that any method would work to some extent in a problem like yours. I would start with something like Hooke-Jeeves

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2 Recommendations

Ashkan Memari

Central Queensland University

Mohamed B Trabia:

Hi

Thank you for reply. But I think it is difficult to apply to my model. What is your next suggestion?

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1 Recommendation

Mohamed B Trabia

University of Nevada, Las Vegas

I think it is doable by modifying any of the simple search algorithms to fit your model

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1 Recommendation

Ashkan Memari

Central Queensland University

what is the advantages of this method vs others local search such as simulated annealing?

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1 Recommendation

Mohamed B Trabia

University of Nevada, Las Vegas

Simulated annealing is a global method similar to GA. You make the search hybrid to enhance speed or to identify additional minima

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1 Recommendation

Ashkan Memari

Central Queensland University

I would like to increase the performance measures of multi-objective optimization like generational distance (GD), Spacing and son on. It would be great if both performance & speed boost up.

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1 Recommendation

Alireza Soroudi

The Institution of Engineering and Technology

I had some good experienced using Immune algorithm hybrid with GA. 

http://scholar.google.com/citations?view_op=view_citation&hl=en&user=w74f9yAAAAAJ&sortby=pubdate&citation_for_view=w74f9yAAAAAJ:7PzlFSSx8tAC

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3 Recommendations

Satyam Sharma

Punjab Technical University

Kindly refer to this:

S. Sharma, R. Kapoor and S. Dhiman, "A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization,"2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC), 2021, pp. 376-381, doi: 10.1109/ICSCCC51823.2021.9478142.

Or refer to the same paper at the following address:

https://www.researchgate.net/publication/353403100_A_Novel_Hybrid_Metaheuristic_Based_on_Augmented_Grey_Wolf_Optimizer_and_Cuckoo_Search_for_Global_Optimization

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1 Recommendation

Nancy Ann Watanabe

University of Oklahoma-Norman

Although I am only just now beginning to look into hybrid studies, this link may be of some interest in the area of hybrid metaheuristics:

"📷

Applied Soft Computing

Volume 11, Issue 6, September 2011, Pages 4135-4151

📷

Hybrid metaheuristics in combinatorial optimization: A survey

Author links open overlay panel-Christian-BlumaJ-akob-Puchingerb-Günther R.Raidlc-Andrea-Rolid

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https://doi.org/10.1016/j.asoc.2011.02.032Get rights and content

Abstract

Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples."

Source: https://www.sciencedirect.com/science/article/abs/pii/S1568494611000962

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2 Recommendations

Nancy Ann Watanabe

University of Oklahoma-Norman

This link to an Abstract for a scientific research article on Springer has the benefit of displaying a list of reference works:

"📷

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Hybrid Metaheuristics📷

Part of the Studies in Computational Intelligence book series (SCI, volume 114)Hybrid Metaheuristics pp 1-30| Cite asHybrid Metaheuristics: An Introduction Authors Authors and affiliations Christian Blum Andrea Roli 1. 2. Chapter49Citations 1.6kDownloads In many real life settings, high quality solutions to hard optimization problems such as flight scheduling or load balancing in telecommunication networks are required in a short amount of time. Due to the practical importance of optimization problems for industry and science, many algorithms to tackle them have been developed. One important class of such algorithms are metaheuristics. The field of metaheuristic research has enjoyed a considerable popularity in the last decades. In this introductory chapter we first provide a general overview on metaheuristics. Then we turn towards a new and highly successful branch of metaheuristic research, namely the hybridization of metaheuristics with algorithmic components originating from other techniques for optimization. The chapter ends with an outline of the remaining book chapters.Keywords Search Space Local Search Constraint Satisfaction Problem Memetic Algorithm Greedy Randomize Adaptive Search ProcedureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.This is a preview of subscription content, log in to check access. Preview References

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SOURCE: Chapter Hybrid Metaheuristics: An Introduction

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1 Recommendation

Nancy Ann Watanabe

University of Oklahoma-Norman

This is another scientific research article which I located on the Springer website, and, again, you might find a relevant item listed in the reference section at the end.

Conference Paper A Unified View on Hybrid Metaheuristics

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Hybrid Metaheuristics📷

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4030)International Workshop on Hybrid MetaheuristicsHM 2006: Hybrid Metaheuristics pp 1-12| Cite asA Unified View on Hybrid Metaheuristics Authors Authors and affiliations Günther R. Raidl 1. Conference paper112Citations 1kDownloads Abstract Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to low-level hybrids of different metaheuristics, a unified view based on a common pool template is described. It helps in making similarities and different key components of existing metaheuristics explicit. We then consider these key components as a toolbox for building new, effective hybrid metaheuristics. This approach of thinking seems to be superior to sticking too strongly to the philosophies and historical backgrounds behind the different metaheuristic paradigms. Finally, particularly promising possibilities of combining metaheuristics with constraint programming and integer programming techniques are highlighted.

Keywords Local Search Integer Linear Programming Constraint Programming Memetic Algorithm Greedy Randomized Adaptive Search Procedure

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work is supported by the European RTN ADONET under grant 504438.This is a preview of subscription content, log in to check access.

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Copyright information © Springer-Verlag Berlin Heidelberg 2006

About this paper

Cite this paper as: Raidl G.R. (2006) A Unified View on Hybrid Metaheuristics. In: Almeida F. et al. (eds) Hybrid Metaheuristics. HM 2006. Lecture Notes in Computer Science, vol. 4030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890584_1

• DOIhttps://doi.org/10.1007/11890584_1
• Publisher Name: Springer, Berlin, Heidelberg
• Print ISBN978-3-540-46384-9
• Online ISBN978-3-540-46385-6
• eBook Packages: Computer ScienceComputer Science (R0)

Source: Conference Paper A Unified View on Hybrid Metaheuristics

Cite

3 Recommendations

Mehdi Neshat

University of South Australia

Dear Ashkan,

I would be happy to share my experience in the hybrid optimisation methods and local search in solving the real-engineering problem. Please find some of my publication as follows:

Article New Insights into Position optimisation of Wave Energy Conve...

Article A Hybrid Cooperative Co-evolution Algorithm Framework for Op...

Preprint Optimization of Large Wave Farms Using a Multi-Strategy Evol...

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