Abstract
Meta-heuristic algorithms have become an arising field of research in recent years. Some of these algorithms have proved to be efficient in solving combinatorial optimization problems, particularly knapsack problem. In this paper, four meta-heuristic algorithms are presented particle swarm optimization, firefly algorithm, flower pollination algorithm and monarch butterfly optimization in solving knapsack problem as example of NP-hard combinational optimization problems. Based on twenty 0-1 knapsack problem instances, the computational results demonstrated that the binary flower pollination algorithm has the ability to find the best solutions in reasonable time.
Highlights
Combinatorial optimization “problem is a mathematical study of finding optimal solution from a finite set of objects
Every meta-heuristic algorithm consists of a set of initial population or initial solutions, the sequence of solutions is examined step by step based on randomization and some specified rules to reach the optimal solution
The aim of this paper is to investigate the effectiveness of the nature inspired meta-heuristic algorithms when dealing with a combinatorial optimization problem such as 0-1 knapsack problem
Summary
Combinatorial optimization “problem is a mathematical study of finding optimal solution from a finite set of objects. The popularity of combinatorial optimization problems comes from the fact that the objective function and constraints in many real-world problems have a different nature (nonlinear, nonanalytic, etc.), while the search space is finite. In such problems, exact methods are impractical in finding an optimal solution because the run time is increasing exponentially with the problem size. The aim of this paper is to investigate the effectiveness of the nature inspired meta-heuristic algorithms when dealing with a combinatorial optimization problem such as 0-1 knapsack problem
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