EXPERIMENTAL ANALYSIS BASED ON TIME COMPLEXITY AND SOLUTION QUALITY OF SOME SWARM INTELLIGENCE ALGORITHMS

Abstract

The most popular tool nowadays for solving multidimensional optimization problems are nature
inspired algorithms. These algorithms as the name implies are inspired by nature; nonetheless,
they also have their own merit and demerit. Swarm intelligence-based optimization algorithms in
recent-times are gaining more popularity for their resource efficiency and effectiveness.
However, randomly picking an algorithm to help solve a problem or answer a question cannot
guarantee quality of solution and or time efficiency. Therefore, this paper analyzes the timecomplexity
and efficacy of some nature-inspired algorithms which include Artificial Bee Colony
(ABC), Bat Algorithm (BA), and Particle Swarm Optimization (PSO). For each algorithm,
experiments were conducted several times and comparative analyses were made. It was found
out that Artificial Bee Colony (ABC) outperformed both the other algorithms (BA and PSO) in
terms of quality of the solution; Particle Swarm Optimization (PSO) is time-efficient while
Artificial Bee Colony yields a worst-case scenario in terms of time complexity. Bat Algorithm
on the other hand presents a worst case in terms of quality of solution and average case with
respect to time complexity.