.png){kind=icon}
.png){kind=icon}
Return
Bio-Inspired Ant Lion Optimizer for a Constrained Petroleum Product Scheduling
.png){kind=icon}
OA
AI
C
D
T
F
B
DOI：10.1109/ACCESS.2022.3204811.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Real-world optimization problems demand sophisticated algorithms. Over the years bio-inspired approach, a subset of computational intelligence has demonstrated remarkable success in real-world use cases, especially where exact or deterministic algorithms are ineffective. Petroleum product scheduling is a complex optimization task belonging to the combinatorial problem category. The problem size and the constraints compound the complexity of the petroleum product scheduling problem. However, conventional optimization methods such as the exact or deterministic algorithm produced a poor solution quality to the petroleum products scheduling problem. Therefore, this study leverages the potency of a bio-inspired approach, Ant Lion Optimizer (ALO) in its basic state to enhance the solution quality. This is in line with She-Shin Yang's proposition, father of bio-inspired algorithms who advocated for the application of existing bio-inspired algorithms to tackle real-world problems rather than developing new algorithms. Bio-inspired is a computational paradigm that models the characteristics of natural biological entities to solve complex problems. We also used the Chaotic Particle Swarm Optimization (CPSO) algorithm for the same problem to unveil the efficacy of the roulette wheel function in ALO. The results show a 24.8% and 23.9% reduction in the original cost of distribution on ALO and CPSO respectively. Also, 99.5% of the constraints are met. Thus, problems of scarcity, minimum allocation and product availability are solved using the penalty constraint handling method. The exact algorithm showed a 14% reduction in the original cost. However, despite the effectiveness, further work on constraint handling methods and other bio-inspired computation approaches such as Genetic algorithms and their variants could be possible in the future scope. Moreover, other real-world problem domains such as power distribution in the power, sector could be a possible application of the ALO.
Keywords:
Optimization
Petroleum industry
Costs
Constraint handling
Scheduling
Particle swarm optimization
Genetic algorithms
Bio-inspired computing
Processor scheduling
Resource management
Computational intelligence
Optimization
bio-inspired algorithms
ant lion optimizer
particle swarm optimizer
petroleum product scheduling
chaotic functions
penalty constraint handling method
Journal
IF:
3.6
Papers:
9.7W
Citations:
29.4W
Organization
Cited Papers