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Recent Developments in Metaheuristics 1st ed. 2018 [Kõva köide]

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  • Formaat: Hardback, 496 pages, kõrgus x laius: 235x155 mm, kaal: 8985 g, 75 Illustrations, color; 115 Illustrations, black and white; XXIII, 496 p. 190 illus., 75 illus. in color., 1 Hardback
  • Sari: Operations Research/Computer Science Interfaces Series 62
  • Ilmumisaeg: 02-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319582526
  • ISBN-13: 9783319582528
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Recent Developments in Metaheuristics 1st ed. 2018Suurem pilt
  • Formaat: Hardback, 496 pages, kõrgus x laius: 235x155 mm, kaal: 8985 g, 75 Illustrations, color; 115 Illustrations, black and white; XXIII, 496 p. 190 illus., 75 illus. in color., 1 Hardback
  • Sari: Operations Research/Computer Science Interfaces Series 62
  • Ilmumisaeg: 02-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319582526
  • ISBN-13: 9783319582528
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319582528.html
Märksõnad:
This book highlights state-of-the-art developments in metaheuristics research. It examines all aspects of metaheuristic research including new algorithmic developments, applications, new research challenges, theoretical developments, implementation issues, in-depth experimental studies. The book is divided into two sections. Part I is focused on new optimization and modeling techniques based on metaheuristics. The chapters in this section cover topics from multi-objective problems with fuzzy data with triangular-valued objective functions, to hyper-heuristics optimization methodology, designing genetic algorithms, and also the cuckoo search algorithm. The techniques described help to enhance the usability and increase the potential of metaheuristic algorithms. Part II showcases advanced metaheuristic approaches to solve real-life applications issues. This includes an examination of scheduling, the vehicle routing problem, multimedia sensor network, supplier selection, bin packing, objects tracking, and radio frequency identification. In the fields covered in the chapters are of high-impact applications of metaheuristics. The chapters offer innovative applications of metaheuristics that have a potential of widening research frontiers. Altogether, this book offers a comprehensive look at how researchers are currently using metaheuristics in different domains of design and application.

Arvustused

The chapters in the book contain adequate references to the literature for further study. The index is also quite helpful. The book sheds light on recent developments in the field of metaheuristics with emphases on algorithms, applications, thought-provoking questions, theory, and implementation aspects. I recommend this book for the aforementioned categories of readers. (Computing Reviews, April, 2018)

1 Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization
1(16)
Oussama Aoun
Malek Sarhani
Abdellatif El Afia
1.1 Introduction
1(1)
1.2 Literature Review
2(3)
1.3 Classification of APSO States by HMM
5(5)
1.3.1 Adaptive PSO Framework
5(1)
1.3.2 HMM Classification of Particle States
6(4)
1.4 Experiment
10(4)
1.4.1 Parameters Setting
10(1)
1.4.2 Comparison on the Solution Accuracy
11(1)
1.4.3 Comparison on the Convergence Speed
12(2)
1.5 Conclusion
14(3)
References
14(3)
2 Possibilistic Framework for Multi-Objective Optimization Under Uncertainty
17(26)
Oumayma Bahri
Nahla Ben Amor
El-Ghazali Talbi
2.1 Introduction
17(1)
2.2 Background on Deterministic Multi-Objective Optimization
18(4)
2.3 Existing Approaches for Uncertain Multi-Objective Optimization
22(1)
2.4 Proposed Possibilistic Framework for Multi-Objective Problems Under Uncertainty
23(10)
2.4.1 Basics on Possibility Theory
23(3)
2.4.2 Adaptation of Possibilistic Setting
26(1)
2.4.3 New Pareto Optimality over Triangular Fuzzy Numbers
27(4)
2.4.4 Extended Optimization Algorithm
31(2)
2.5 Application on a Multi-Objective Vehicle Routing Problem
33(6)
2.6 Conclusion
39(4)
References
40(3)
3 Combining Neighborhoods into Local Search Strategies
43(16)
Renaud De Landtsheer
Yoann Guyot
Gustavo Ospina
Christophe Ponsard
3.1 Introduction
43(2)
3.2 Related Work
45(1)
3.3 Principle of Neighborhood Combinators
46(1)
3.4 Six Shades of Warehouse Location
47(4)
3.5 Building a Library of Combinators
51(3)
3.5.1 Neighborhood and Move Selection Combinators
51(1)
3.5.2 Acceptation Function Combinators
52(1)
3.5.3 Solution Management Combinators
53(1)
3.5.4 Stop Criterion Combinators
53(1)
3.5.5 Code Embedding Combinators
54(1)
3.5.6 Neighborhood Aggregation Combinators
54(1)
3.6 A Vehicle Routing Example with Combinators
54(1)
3.7 Conclusion
55(4)
References
57(2)
4 All-Terrain Tabu Search Approaches for Production Management Problems
59(16)
Nicolas Zufferey
Jean Respen
Simon Thevenin
4.1 Introduction
59(2)
4.2 Smoothing the Production for Car Sequencing
61(2)
4.2.1 Presentation of the Problem
61(1)
4.2.2 Solution Methods
62(1)
4.2.3 Experiments
62(1)
4.3 A Deconstruction-Reconstruction Method for Job Scheduling
63(3)
4.3.1 Presentation of the Problem
63(1)
4.3.2 Solution Methods
64(1)
4.3.3 Experiments
65(1)
4.4 Tabu Search with Diversity Control and Simulation
66(3)
4.4.1 Presentation of the Problem
66(1)
4.4.2 Solution Methods
67(1)
4.4.3 Experiments
68(1)
4.5 Dynamic Tabu Search for a Resource Allocation Problem
69(3)
4.5.1 Presentation of Dynamic Tabu Search
69(2)
4.5.2 Application to a Resource Allocation Problem
71(1)
4.6 Conclusion
72(3)
References
72(3)
5 A Re-characterization of Hyper-Heuristics
75(16)
Jerry Swan
Patrick De Causmaecker
Simon Martin
Ender Ozcan
5.1 Introduction
76(4)
5.1.1 Historical Development of Hyper-Heuristics
76(3)
5.1.2 Effectiveness in New Domains
79(1)
5.2 Popular Notion of the Domain Barrier
80(2)
5.3 The Need for `Domain-Independent Domain Knowledge'
82(2)
5.4 Cross-Domain Knowledge Representation
84(2)
5.5 Future Directions: The Role of Ontologies
86(1)
5.6 Conclusion
87(4)
References
87(4)
6 POSL: A Parallel-Oriented Metaheuristic-Based Solver Language
91(18)
Alejandro Reyes-Amaro
Eric Monfroy
Florian Richoux
6.1 Introduction
91(2)
6.2 POSL Parallel Solvers
93(7)
6.2.1 Operation Module
93(1)
6.2.2 Open Channels
94(1)
6.2.3 Computation Strategy
95(4)
6.2.4 Solver Definition
99(1)
6.2.5 Communication Definition
99(1)
6.3 A POSL Solver
100(3)
6.3.1 Connecting Solvers
102(1)
6.4 Results
103(3)
6.5 Conclusions
106(3)
References
107(2)
7 An Extended Neighborhood Vision for Hill-Climbing Move Strategy Design
109(16)
Sara Tari
Matthieu Basseur
Adrien Goeffon
7.1 Introduction
109(1)
7.2 Combinatorial Optimization, Local Search, Hill-Climbing
110(2)
7.3 Hill-Climbing Moving Strategies: Description and Evaluation
112(7)
7.3.1 Context
112(1)
7.3.2 Experimental Protocol
113(1)
7.3.3 Maximum Expansion vs. A1 Vision Area Climbers
114(3)
7.3.4 Maximum Expansion vs. A2 Vision Area Climbers
117(2)
7.4 Maximum Expansion Sophistication: A Multiobjectivized Approach
119(3)
7.4.1 Multiobjectivization
120(1)
7.4.2 Biobjectivized Pivoting Rules
121(1)
7.5 Discussion
122(3)
References
123(2)
8 Theory Driven Design of Efficient Genetic Algorithms for a Classical Graph Problem
125(16)
Dogan Corns
Per Kristian Lehre
8.1 Introduction
125(2)
8.2 Analysis of EAs on Shortest Path Problems
127(2)
8.3 Method: Level-Based Analysis
129(2)
8.4 Design of a Genetic Algorithm
131(6)
8.4.1 Representation of Solutions
132(1)
8.4.2 The Objective Function and Level Structure
132(2)
8.4.3 The Mutation Operator
134(1)
8.4.4 The Crossover Operator
135(1)
8.4.5 Other Parameter Settings
136(1)
8.4.6 All-Pairs Shortest Path Problem
136(1)
8.5 Expected Running Time
137(1)
8.6 Conclusion
138(3)
References
139(2)
9 On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework
141(10)
Eric S. Fraga
Abdellah Salhi
El-Ghazali Talbi
9.1 Introduction
142(1)
9.2 Representation
142(1)
9.3 Motivating Example
143(2)
9.4 The Match-Making Problem
145(1)
9.5 Heat Exchanger Network Design
145(1)
9.6 Representations and Algorithms for HENS
146(1)
9.7 Results
147(1)
9.8 Conclusions
148(3)
References
149(2)
10 Manufacturing Cell Formation Problem Using Hybrid Cuckoo Search Algorithm
151(12)
Bouchra Karoum
Bouazza Elbenani
Noussaima El Khattabi
Abdelhakim A. El Imrani
10.1 Introduction
151(2)
10.2 Problem Formulation
153(1)
10.3 Improved Cuckoo Search Algorithm
154(4)
10.3.1 Basic Cuckoo Search
154(1)
10.3.2 The Proposed Cuckoo Search Algorithm
155(3)
10.4 Computational Results
158(3)
10.5 Conclusion
161(2)
References
161(2)
11 Hybridization of Branch and Bound Algorithm with Metaheuristics for Designing Reliable Wireless Multimedia Sensor Network
163(16)
Omer Ozkan
Murat Ermis
Ilker Bekmezci
11.1 Introduction
163(2)
11.2 The Problem Definition
165(4)
11.3 Hybridization of Branch and Bound Algorithm with Metaheuristics
169(3)
11.3.1 Hybridization of Branch and Bound with Simulated Annealing
170(1)
11.3.2 Hybridization of Branch and Bound with Genetic Algorithm
171(1)
11.4 Experimental Study
172(5)
11.4.1 Parameter Tuning
174(1)
11.4.2 Performance Results
174(3)
11.5 Conclusion
177(2)
References
177(2)
12 A Hybrid MCDM Approach for Supplier Selection with a Case Study
179(20)
Hanane Assellaou
Brahim Ouhbi
Bouchra Frikh
12.1 Introduction
179(2)
12.2 Related Works
181(2)
12.3 Methodology
183(5)
12.3.1 DEMATEL Method
184(1)
12.3.2 ANP Method
185(2)
12.3.3 TOPSIS Method
187(1)
12.4 Proposed Methodology and Application Case
188(7)
12.4.1 Identification of Necessary Criteria for Supplier Selection
189(1)
12.4.2 Applying DEMATEL for Constructing the Interdependence Relationship Network
190(1)
12.4.3 The Weights of Criteria Calculation
191(2)
12.4.4 Application of TOPSIS in Alternatives Ranking
193(2)
12.5 Conclusion
195(4)
References
196(3)
13 A Multi-Objective Optimization via Simulation Framework for Restructuring Traffic Networks Subject to Increases in Population
199(20)
Enrique Gabriel Baquela
Ana Carolina Olivera
13.1 Introduction
199(2)
13.1.1 Literature Review
200(1)
13.2 Origin-Destiny Traffic Assignment Problem
201(3)
13.2.1 Traffic Systems
201(2)
13.2.2 Origin-Destiny Traffic Assignment Problem
203(1)
13.3 Traffic Simulations
204(1)
13.4 Multiobjective Particle Swamp Optimization
205(2)
13.4.1 Particle Swamp Optimization
205(1)
13.4.2 Multi-Objective Particle Swamp Optimization
206(1)
13.5 Optimization Framework
207(2)
13.5.1 General Procedure
207(1)
13.5.2 Solution Evaluation
208(1)
13.6 Experiments
209(2)
13.6.1 Scenarios
209(1)
13.6.2 Tests
210(1)
13.6.3 Algorithm Parameters
211(1)
13.7 Results
211(4)
13.7.1 Convergence
211(2)
13.7.2 Uniformity
213(1)
13.7.3 Evolution of the Solutions Generated by the Algorithm
213(2)
13.7.4 Comparison with NSGA-II Metaheuristic
215(1)
13.8 Conclusions
215(4)
References
216(3)
14 Hybrid Metaheuristic for Air Traffic Management with Uncertainty
219(34)
S. Chaimatanan
D. Delahaye
M. Mongeau
14.1 Introduction
219(5)
14.1.1 Air Traffic Management: A Brief Review
220(2)
14.1.2 Strategic Aircraft Trajectory Planning
222(2)
14.2 Previous Related Works
224(2)
14.3 Mathematical Model
226(11)
14.3.1 Uncertainty
227(2)
14.3.2 Trajectory Separation Methods
229(3)
14.3.3 Optimization Formulation
232(5)
14.4 Interaction Detection Module
237(3)
14.5 Hybrid-Metaheuristic for Strategic Trajectory Planning
240(6)
14.5.1 Simulated Annealing: A Brief Overview
241(1)
14.5.2 Iterative-Improvement Local Search: A Brief Overview
242(1)
14.5.3 Hybrid Simulated-Annealing/Iterative-Improvement Local Search
243(3)
14.5.4 Neighborhood Function
246(1)
14.6 Computational Results
246(2)
14.7 Conclusions
248(5)
References
250(3)
15 Sampling-Based Genetic Algorithms for the Bi-Objective Stochastic Covering Tour Problem
253(32)
Michaela Zehetner
Walter J. Gutjahr
15.1 Introduction
253(2)
15.2 The Bi-Objective Stochastic Covering Tour Problem
255(6)
15.2.1 The Covering Tour Problem
255(1)
15.2.2 The Bi-Objective Stochastic Extension
255(2)
15.2.3 Literature Review
257(1)
15.2.4 Mathematical Formulation
258(3)
15.3 Algorithms
261(7)
15.3.1 NSGA-II
262(3)
15.3.2 Savings Algorithm
265(1)
15.3.3 Sampling Procedures
265(3)
15.4 Case Study
268(10)
15.4.1 Implementation Details
268(1)
15.4.2 Performance Measures
269(1)
15.4.3 Test Instances
270(1)
15.4.4 Computational Results
271(7)
15.5 Conclusions
278(7)
References
281(4)
16 A Metaheuristic Framework for Dynamic Network Flow Problems
285(20)
M. Hajjem
H. Bouziri
El-Ghazali Talbi
16.1 Introduction
285(1)
16.2 Basic Notions and Results of Static Network Flow Problems
286(1)
16.3 Dynamic Network
287(3)
16.3.1 Time Horizon
287(1)
16.3.2 Parameters
288(1)
16.3.3 Representation
289(1)
16.4 Flow Over Time Models
290(3)
16.4.1 Maximum Dynamic Flow Problem
291(1)
16.4.2 Earliest Arrival Flow Problem
291(1)
16.4.3 Quickest Flow Problem
292(1)
16.4.4 Dynamic Minimum Cost Flow Problem
292(1)
16.4.5 Complexity of Dynamic Network Flow Problems
293(1)
16.5 Metaheuristics
293(2)
16.5.1 Blackboard-Based Metaheuristics
294(1)
16.5.2 Evolutionary-Based Metaheuristics
294(1)
16.6 An Evolutionary Framework for Dynamic Network Flow Problems
295(1)
16.6.1 Solution Representation
295(1)
16.6.2 Generation of Initial Solutions
295(1)
16.6.3 Crossover Operator
295(1)
16.6.4 Mutation Operator
296(1)
16.7 Application to Evacuation Problem
296(6)
16.7.1 A Case Study for Building Evacuation
297(1)
16.7.2 Design of Genetic Algorithm
298(2)
16.7.3 Results Analysis
300(2)
16.8 Conclusion
302(3)
References
303(2)
17 A Greedy Randomized Adaptive Search for the Surveillance Patrol Vehicle Routing Problem
305(14)
Simona Mancini
17.1 Introduction
305(2)
17.2 Literature Review
307(1)
17.3 The Surveillance Patrols Vehicle Routing Problem
308(1)
17.4 A GRASP for the Surveillance Patrol Vehicle Routing Problem
309(4)
17.4.1 Local Search
312(1)
17.4.2 Feasibility Search
313(1)
17.5 A GRASP for Solutions Pools Selection
313(1)
17.6 Computational Tests
314(1)
17.7 Conclusions and Future Developments
315(4)
References
316(3)
18 Strip Algorithms as an Efficient Way to Initialise Population-Based Metaheuristics
319(14)
Birsen Irem Selamoglu
Abdellah Salhi
Muhammad Sulaiman
18.1 Introduction
319(1)
18.2 Ideas Behind the Strip Algorithm
320(3)
18.2.1 The Appropriate Number of Strips
322(1)
18.3 2-PSA: The 2-Part Strip Algorithm
323(1)
18.4 Worst-Case Analysis of 2-PSA and an Upper Bound on the Minimum Tour Lengths Returned
323(2)
18.5 Other Implementations of the Strip Algorithm
325(2)
18.5.1 The Adaptive Strip Algorithm (ASA)
326(1)
18.5.2 The Spiral Strip Algorithm (SSA)
326(1)
18.6 Computational Results and Conclusion
327(6)
References
330(3)
19 Matheuristics for the Temporal Bin Packing Problem
333(14)
Fabio Furini
Xueying Shen
19.1 Introduction
333(2)
19.1.1 Greedy-Type Algorithms
334(1)
19.2 Compact Formulation
335(3)
19.3 Extensive Formulation
338(2)
19.3.1 Column Generation Heuristics
339(1)
19.4 Computational Experiments
340(5)
19.5 Conclusion
345(2)
References
345(2)
20 A Fast Reoptimization Approach for the Dynamic Technician Routing and Scheduling Problem
347(22)
V. Pillac
C. Gueret
A.L. Medaglia
20.1 Introduction
348(1)
20.2 Literature Review
349(2)
20.3 The Parallel Adaptive Large Neighborhood Search
351(5)
20.3.1 Destroy
352(2)
20.3.2 Repair
354(1)
20.3.3 Adaptive Layer
354(1)
20.3.4 Objective Function
355(1)
20.3.5 Acceptance Criterion
355(1)
20.3.6 Computation of an Initial Solution
356(1)
20.3.7 Solution Pool
356(1)
20.4 Parallel Reoptimization Approach
356(2)
20.5 Computational Results
358(7)
20.5.1 Experimental Setting
358(1)
20.5.2 Validation on the D-VRPTW
359(2)
20.5.3 Results on the D-TRSP
361(4)
20.6 Conclusions
365(4)
References
365(4)
21 Optimized Air Routes Connections for Real Hub Schedule Using SMPSO Algorithm
369(16)
H. Rahil
B. Abou El Majd
M. Bouchoum
21.1 Introduction
369(3)
21.2 Methods
372(7)
21.2.1 Problem Formulation
372(5)
21.2.2 SMPSO Algorithm
377(2)
21.3 Results and Discussion
379(3)
21.4 Conclusions and Future Works
382(3)
References
383(2)
22 Solving the P/Prec, pj, Cij/Cmax Using an Evolutionary Algorithm
385(14)
Dalila Tayachi
22.1 Introduction
385(1)
22.2 Problem Formalization
386(1)
22.3 Related Work
387(1)
22.4 A New Hybrid Evolutionary Algorithm Based on Particle Swarm Optimization HEA-LS
388(5)
22.4.1 Particle Swarm Optimization
388(1)
22.4.2 Position Representation and Fitness Evaluation
389(1)
22.4.3 Position Update
390(1)
22.4.4 The Hybrid HEA-LS Algorithm
391(2)
22.5 Performance Comparison and Results
393(3)
22.6 Conclusion and Perspectives
396(3)
References
397(2)
23 A User Experiment on Interactive Reoptimization Using Iterated Local Search
399(16)
David Meignan
23.1 Introduction
399(2)
23.2 Interactive Reoptimization for Shift Scheduling
401(5)
23.2.1 Interactive Process
401(1)
23.2.2 Shift Scheduling
402(2)
23.2.3 Optimization Procedures
404(2)
23.3 Experiment
406(4)
23.3.1 Objectives
406(1)
23.3.2 Method
407(3)
23.4 Results
410(2)
23.5 Conclusion and Perspectives
412(3)
References
413(2)
24 Surrogate-Assisted Multiobjective Evolutionary Algorithm for Fuzzy Job Shop Problems
415(14)
Juan Jose Palacios
Jorge Puente
Camino R. Vela
Ines Gonzalez-Rodriguez
El-Ghazali Talbi
24.1 Introduction
415(2)
24.2 Job Shop Scheduling with Uncertain Durations
417(3)
24.2.1 Uncertain Durations
417(1)
24.2.2 Robust Scheduling
418(1)
24.2.3 The Multiobjective Approach
419(1)
24.3 Multiobjective Evolutionary Algorithm
420(2)
24.3.1 The Surrogate Model
420(2)
24.3.2 Genetic Operators
422(1)
24.4 Experimental Study
422(4)
24.5 Conclusions
426(3)
References
427(2)
25 Towards a Novel Reidentification Method Using Metaheuristics
429(18)
Tarik Ljouad
Aouatif Amine
Ayoub Al-Hamadi
Mohammed Rziza
25.1 Introduction
430(1)
25.2 Object Representation
431(3)
25.3 Projection of the Modified Cuckoo Search on the Multiple Object Tracking Problem
434(5)
25.4 Experimental Results
439(4)
25.5 Conclusion
443(4)
References
444(3)
26 Facing the Feature Selection Problem with a Binary PSO-GSA Approach
447(16)
Malek Sarhani
Abdellatif El Afia
Rdouan Faizi
26.1 Introduction
447(2)
26.2 Literature Review
449(1)
26.3 The Proposed Binary PSOGSA Approach
450(5)
26.3.1 The Canonical BPSO Algorithm
450(1)
26.3.2 The Classical BGSA Algorithm
451(1)
26.3.3 The Aggregative Multi-Objective Fitness Function of FS
452(1)
26.3.4 The Proposed Hybrid Algorithm for FS
452(3)
26.4 Experiment
455(6)
26.4.1 Experiment Setup
455(1)
26.4.2 Examination of BMPSOGSA for FS
456(4)
26.4.3 Comparison with Weil-Known FS Techniques
460(1)
26.5 Conclusion
461(2)
References
461(2)
27 An Optimal Deployment of Readers for RFID Network Planning Using NSGA-II
463(14)
Abdelkader Raghib
Badr Abou El Majd
Brahim Aghezzaf
27.1 Introduction
463(2)
27.2 Problem Formulation
465(2)
27.2.1 Number of Deployed Readers
466(1)
27.2.2 Full Coverage
466(1)
27.2.3 Interference
467(1)
27.3 Methods
467(3)
27.3.1 NSGA-II Algorithm
468(1)
27.3.2 The Proposed Approaches
469(1)
27.4 Results and Discussion
470(5)
27.5 Conclusion
475(2)
References
475(2)
28 An Enhanced Bat Echolocation Approach for Security Audit Trails Analysis Using Manhattan Distance
477(18)
Wassila Guendouzi
Abdelmadjid Boukra
28.1 Introduction
477(1)
28.2 Related Work
478(1)
28.3 Problem Formulation
479(1)
28.4 Bat Algorithm Overview
480(1)
28.5 Proposed Approach
481(6)
28.5.1 Solution Representation
482(1)
28.5.2 Initialization of Algorithm Parameters and Bat Population
482(1)
28.5.3 Fitness Function and Manhattan Distance
482(2)
28.5.4 Proposed Discretisation
484(1)
28.5.5 Operators
485(2)
28.6 Experimental Results
487(4)
28.6.1 Performance Validation Using Random Data
488(2)
28.6.2 Comparisons of EBBA with BBO, GA and HS Algorithms Using Real Data
490(1)
28.7 Conclusion
491(4)
References
492(3)
Index 495
Lionel Amodeo obtained his Engineering Degree in Mechanical Engineering from the National Engineer School of Belfort (France) in 1993, and the same year, his masters degree in Automatic and Production Management from the University of Franche Comté (France). In 1999, he received his Ph.D. degree in Automatic and Computer Sciences from the University of Franche Comté (France). Then he became an Associated Professor at the University of Technology of Troyes (UTT) in 2000. Since 2010, he is a full Professor at UTT, where he is the head of the Engineer Degree in Industrial Systems with more than 400 students. His research interests include logistic and production systems optimization, scheduling, system design, facility layout and inventory problems. He has published more than 260 contributions including 4 books, 11 book chapters, 38 papers in international journals and over 190 communications in conference proceedings. He is member of Editorial Board of journals: International Journal of Engineering Mathematics, Hindawi Publishing Corporation, Journal of Modelling and Simulation of Systems, Journal of Studies on Manufacturing. He is also member of several international conference committees (about 25 conferences) (MIC2017, MIM 2016, INCOM2015, IESM 2015), member of more than 15 organization committees of conferences. He organized and or Chair more than 30 tracks or sessions in conferences. He is reviewer for more than 18 international journals. He supervises or supervised 16 Ph.D thesis and 32 Master of Sc. Student. He is expert for French Research Agency (ANR), and for different agencies such as French AERES Agency, Natural Sciences and Technology Research Fund (FRQNT, Canada), and Austrian Research Fund (FWF). He is member of the Technical Committee IFAC T.7.4 Transportation Systems. El-ghazali Talbi received the Master and Ph.D degrees in Computer Science, both from the Institut NationalPolytechnique de Grenoble in France. Then he became an Associate Professor in Computer Sciences at the University of Lille (France). Since 2001, he is a full Professor at the University of Lille and the head of the optimization team of the Computer Science laboratory (CRISTAL). His current research interests are in the field of multi-objective optimization, parallel algorithms, metaheuristics, combinatorial optimization, cluster and grid computing, hybrid and cooperative optimization, and application to logistics/transportation and bio-medical. Professor Talbi has to his credit more than 150 publications in journals, chapters in books, and conferences. He is the co-editor of 6 books. He was a guest editor of more than 15 special issues in different journals (Journal of Heuristics, Journal of Parallel and Distributed Computing, European Journal of Operational Research, Theoretical Computer Science, Journal of Global Optimization). He is the head of the INRIA Dolphin project and was the director of the bioinformatics platform of the Genopole of Lille. He has many collaborative national, European and international projects. He is the co-founder and the coordinator of the research group dedicated to Metaheuristics: Theory and Applications (META). He is the founding co-chair of the NIDISC workshop on nature inspired computing (IEEE/ACM IPDPS) and META (Int. Conf. on Metaheuristics). He served in different capacities on the programs of more than 100 national and international conferences. He is also the organizer of many conferences (e.g. EA'2005, ROADEF'2006, META'2008, IEEE AICCSA'2010, META'2014, MIC'2015, META2016, BIOMA2018). Farouk Yalaoui obtained his Engineering degree in Industrial Engineering from the Polytechnics School of Algiers (Algeria) in 1995, his masters degree in Industrial System Engineering from Polytechnics Institute of Lorraine (Nancy, France) in 1997, his Ph.D. degree in Production Management from the Troyes University of Technology (UTT) in 2000 and followed by a Habilitation à diriger les recherches (Dr. Hab) from Compiegne University of Technology (UTC) in 2006. He is currently a full Professor at Troyes University of Technology, France, where he is the head of Optimisation Industrial systems Optimisation Lab (Research Team), Charles Dealaunay Institute (ICD), UMR CNRS 6281. His research topic focuses on the scheduling problems, system design, operations research, modeling, analysis and optimization of logistic and production systems, reliability and maintenance optimization and on optimization problems in general. He is author or co-author of a pattern and more than 330 contributions, publications or communications with 1 patent; 3 books (Ellipses, Hermes-Lavoisier, Willey and Sons), 9 book chapters and 60 papers in journals such as IIE Transactions, European Journal of Operational research, International Journal of Production Economics, IEEE Transactions on Reliability, Reliability Engineering and System Safety, Computer & Operations research, Journal of Intelligent Manufacturing. He also published more than 230 papers in conference proceedings. He had presented 19 invited speeches (seminaries or conferences plenary sessions). He is member of editor board of the book series "Automation and Control - Industrial Engineering", ISTE Wiley, London, since 2014. He is also member of several international conference committees (about 83 conferences) (INCOM2015, IESM 2015, MIC2015,), member more than 25 organization committees of conferences (ICIST, ROADEF, IFAC 2016). He served or serves as editor to 15 International journal Boards: The Scientific World Journal as part of the journal's Operations Research subject area (TSWJ), International journal of Supply and Operations Management (IJSOM), British Journal of Mathematics &computer Science (BJMCS), Journal of Risk Analysis and Crises Response (JRACR). He served as guest editor for Journal special issue (Journal of Multiple Valued Logic and Soft Computing, Journal of Intelligent Manufacturing). He organized and or Chair more than 79 tracks or sessions in conference. He is reviewer for more than 36 international journals. He is Vice Chair of IFAC TC group 5.2. He is Chair of a Working Group on multi objective optimization. He is member of French Universities National Council (CNU) field Automation, Control, Industrial Engineering (section 61). He is member and expert for French ANR agency, and for different agency such French AERES Agency, Natural Sciences and Engineering Research Council of Canada (NSERC), Algerian PNR program, Algerian Academy of Science and Technology.