Scatter Search Methodology and Applications.pdf

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Metaheuristic Classification x/y/z Classification • x = A (adaptive memory) or M (memoryless( memoryless) • y = N (systematic neighborhood search) or S (random sampling) • z = 1 (one current solution) or P (population of solutions) Some Classifications • Tabu search (A/N/1) • Genetic Algorithms (M/S/P) • Scatter Search (M/N/P) 6
Scatter Search Diversification Generation Method Repeat until |P| = PSize P Improvement Method Improvement Method Reference Set Update Method Solution Combination Method Subset Generation Method Stop if no more new solutions RefSet 7
Page 1 and 2: Scatter Search: Methodology and App
Page 3 and 4: Scatter Search Methodology
Page 5: Typical Search Trajectory 100 90 80
Page 9 and 10: Tutorial Unconstrained Nonlinear Op
Page 11 and 12: 11 11 Diverse Solutions Diverse Sol
Page 13 and 14: Reference Set Update Method (Initia
Page 15 and 16: Subset Generation Method All pairs
Page 17 and 18: Alternative Combination Method y 10
Page 19 and 20: Static Update Pool of new trial sol
Page 21 and 22: Tutorial 0-11 Knapsack Problem Maxi
Page 23 and 24: Rebuilding RefSet Rebuilt RefSet b
Page 25 and 26: 3-Tier RefSet Solution Combination
Page 27 and 28: Subsets of Size > 2 90% 80% 70% 60%
Page 29 and 30: Hybrid Approaches Use of Memory •
Page 31 and 32: Multiobjective EA Techniques Indepe
Page 33 and 34: Scatter Search Elements Diversifica
Page 35 and 36: Basic Design Diversification Genera
Page 37 and 38: Main Function int main(void) { SS *
Page 39 and 40: Advanced Design pb = SSProblem_Defi
Page 41 and 42: SSCreate_RefSet /* Order solutions
Page 43 and 44: Advanced Designs Reference Set Upda
Page 45 and 46: An Instance 1 2 3 4 3 4 1 2 1 2 3 4
Page 47 and 48: Diversification Generator Use of pr
Page 49 and 50: Improvement Method INSERT_MOVE (pj(
Page 51 and 52: Experiments with LOLIB 49 Input-Out
Page 53 and 54: OptQuest based Applications Solutio
Page 55 and 56: Comparison with Genocop Test on 28
Page 57 and 58:
Call for Papers European Journal of
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