Unified strategy for intensification and diversification balance in ACO metaheuristic

Abstract

This intensification and diversification in AntColony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation). The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters. This paper introduces the reactive ant colony optimization (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively. Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms. The output of this study can improve the quality of solutions as exemplified by RACO.

Ant-based sorting and ACO-based clustering approaches: A review

“…Exploration is a global search space that produces diverse solutions while exploitation generates information based on the regions exploited in the search on the local region [21]. However, any optimization approach can successfully solve any problem when the balance between the two components is optimal [22,23]. Optimizationbased clustering, applied successfully to solve the clustering problem in different optimization problems, includes Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Simulated Annealing (SA), Firefly (FA) and Ant Colony Optimization (ACO); hybridized with external heuristic algorithms includes K-means, agglomerative and bisect K-means algorithm.…”

Hybrid Ant Colony Optimization and Genetic Algorithm for Rule Induction

“…Pheromone trails represent a distributed, numerical information that the ACO adapt during its execution to reflect the search experience (Dorigo and Stützle, 2004). Many applications involve the usage of ACO metaheuristic framework such as scheduling (Blum, 2005), travel salesman problem (Sagban et al, 2017), assembly line balancing (Blum, 2008), sequential ordering (Dorigo and Stützle, 2010), DNA sequencing (Blum et al, 2008), packet-switched routing (Di Caro and Dorigo, 1998), feature selection (Kanan et al, 2007), data clustering (Jabbar and Ku-Mahamud, 2018;Jabbar et al, 2019a;2019b) and data classification (Al-Behadili et al, 2019;2018b;2018a).…”