Bi-heuristic ant colony optimization-based approaches for traveling salesman problem
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METHODOLOGIES AND APPLICATION
Bi-heuristic ant colony optimization-based approaches for traveling salesman problem Nizar Rokbani1,4 • Raghvendra Kumar3 • Ajith Abraham2 • Adel M. Alimi4 • Hoang Viet Long5,6 Ishaani Priyadarshini7 • Le Hoang Son8,9
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Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Heuristic computational intelligence techniques are widely used in combinatorial optimization problems, essentially in large size configurations. Bio-inspired heuristics such as PSO, FA or FPA showed their capacities to solve such problems. Bi-heuristic optimization consists of using a couple of techniques and a collaboration mechanism. This paper reviews the major contributions in solving TSP with bi-heuristics and presents a new hybridization scheme based on FPA, ACO with Ls, ant supervised by flower pollination with local search, ASFPA-Ls; as well as the impact of social and cognitive PSO for the ant supervised by PSO with local search, ASPSO-Ls. AS-chaotic-PSO-Ls which stands for ant supervised by chaotic PSO local search is also investigated. The meta-heuristic algorithms (FPA, PSO or chaotic PSO) and ant colony optimization are used with a hierarchical collaboration schema in addition to a local search mechanism. In this work, the local search strategy, Ls, used is the 2-opt method; the proposals are called, respectively, ASFPA-Ls, cognitive ant supervised by PSO with local search, Co-ASPSO-Ls, social-ASPSO-Ls and AS-chaotic-PSO-Ls; where ACO is coupled with a local search heuristic mechanism called 2-opt, and a set of meta-heuristics, FA, FPA and PSO asked to adapt ACO parameters while running. Comparative experimental investigations are conducted using the TSP test bench. Proposed hybridizations attended fair solutions for the TSP problems used in the experimental investigations including berlin52, St70, eil76, rat 99, eil101, KroA100, Ch150 and kroA200. A good balance performance/time is found with the social ant supervised by PSO with local search, So-ASPSO-Ls. The cognitive ant supervised by PSO with local search, Co-ASPSO-Ls comes in the second position in terms of time effectiveness with close performances to AS-chaoticPS0-LS, all proposed approaches returned low rate errors or BKS for used test benches: berlin52, st70, eil76, rat99, kroA100 and kroA200. Keywords Bio-inspired heuristics Simplified PSO Local search (ls) Ant supervised by PSO (ASPSO) Traveling salesman problem (TSP)
Communicated by V. Loia. & Hoang Viet Long [email protected] Nizar Rokbani [email protected]
Ishaani Priyadarshini [email protected] Le Hoang Son [email protected] Extended author information available on the last page of the article
Raghvendra Kumar [email protected] Ajith Abraham [email protected] Adel M. Alimi [email protected]
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N. Rokbani et al.
1 Introduction The optimization problem consists of solving problems by minimizing or maximizing their functions (Dhiman 2019). Each function can generate many p
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