TMHSCA: a novel hybrid two-stage mutation with a sine cosine algorithm for discounted {0-1} knapsack problems

Keyphrases

• Novel Hybrids 100%
• Discounted {0-1} Knapsack Problem 100%
• Sine Cosine Algorithm 100%
• Sine Cosine 75%
• Convergence Rate 50%
• Solution Space 25%
• Population Diversity 25%
• Heuristic Algorithms 25%
• Search Strategy 25%
• Greedy Algorithm 25%
• NP-hard Problem 25%
• Search Space 25%
• Algorithm Convergence 25%
• Quasi-greedy Basis 25%
• Infeasible Solution 25%
• Knapsack Problem 25%
• Differential Evolution 25%
• Effective Improvement 25%
• Initial Population 25%
• Algorithm-3 25%
• Repair Strategy 25%
• Mutation Operator 25%
• Art Performance 25%
• Publicly Available Dataset 25%
• Exploitation Ability 25%
• Speed Ability 25%
• Opposition-based Learning Strategy 25%
• Opposition-based Learning 25%
• Random Greedy 25%

Computer Science

• 0-1 knapsack problem 100%
• Convergence Speed 66%
• Experimental Result 33%
• Solution Space 33%
• Metaheuristic Algorithm 33%
• Search Strategies 33%
• Search Space 33%
• Knapsack Problem 33%
• Mutation Operator 33%
• Initial Population 33%
• Differential Evolution 33%
• Art Performance 33%
• Baseline Method 33%
• Greedy Algorithm 33%

Mathematics

• Cosine 100%
• Subpopulation 14%
• Solution Space 14%
• Search Space 14%
• Search Strategies 14%
• Initial Population 14%
• NP-hard Problem 14%
• Greedy Algorithm 14%