The IUP Journal of Computer Sciences
A Comparison of Some Metaheuristic Optimization Algorithms for Solving High Dimensional Benchmark Problems

Article Details
Pub. Date : Jan, 2019
Product Name : The IUP Journal of Computer Sciences
Product Type : Article
Product Code : IJCS21901
Author Name : Parimal Kumar Giri and Chandrakant Mallick
Availability : YES
Subject/Domain : Management
Download Format : PDF Format
No. of Pages : 16



With the emergence of Big Data, the existing optimization techniques need to be tested to find those that are not suitable to handle high dimensional problems. The number of natureinspired population-based metaheuristic optimization algorithms has been explored over the decade with new techniques being proposed constantly. A recent summary of existing algorithms has listed near about 134. A majority of these optimization algorithms have been designed and applied to solve real-parameter function optimization problems, each claimed to be superior to other methods in terms of performance. However, most of these algorithms have been tested on relatively low dimensional problems, i.e., problems involving less than 30 parameters. This paper presents different benchmark functions that are systematically analyzed and tested in detail for problems involving up to 100 parameters. Genetic Algorithms (GA), Biogeography-Based Optimization (BBO) and Particle Swarm Optimization (PSO) are compared in terms of accuracy and runtime using three high dimensional standard benchmark functions.


Optimization plays a significant role in many areas of science, management, economics, bioinformatics and engineering. There are a large number of approaches in mathematics and computer science; and operation research is accessible to explain such problems. However, these approaches have lots of deficiencies in providing quick and accurate solutions when the optimization problem involves numerous variables and constraints. From the time when most real-life troubles can be modeled as optimization tasks, lots of techniques and methods that might undertake such problems were developed. The difficulty of optimization problem depends on the mathematical relationships between the objective function, potential constraints and decision variables. Hard optimization problems can be combinatorial (discrete) or continuous (global optimization), while continuous problems can further be classified as constrained or unconstrained (Weise, 2009).


Metaheuristic, Optimization, Big data, Biogeography

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