A new hybrid heuristic approach for solving large traveling salesman problem
Information Sciences—Informatics and Computer Science: An International Journal
Computing is a natural science
Communications of the ACM - Creating a science of games
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Information Sciences: an International Journal
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Artificial neural network approach for solving fuzzy differential equations
Information Sciences: an International Journal
Cellular particle swarm optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
A multi-level ant-colony mining algorithm for membership functions
Information Sciences: an International Journal
Fluctuation-driven computing on number-conserving cellular automata
Information Sciences: an International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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This paper proposes a new nature-inspired algorithm (NA)-mosquito host-seeking algorithm (MHSA)-the inspiration for which comes from the host-seeking behavior of mosquitoes. Applying the algorithm to the traveling salesman problem (TSP), every city pair is treated as an artificial mosquito, and the TSP solving process is transformed into the host-seeking behavior of a swarm of artificial mosquitoes. We study the evolution of ''swarms'', the artificial mosquitoes' microcosmic actions, and macroscopic swarm intelligence, and present efficient solutions to TSP using MHSA. The proposed MHSA is fundamentally different from the other popular NAs in its motivation, principle, the optimization mechanism, its elements and their states, and the biological model, the mathematical model and theoretical foundation on which it is based. We show that (1) MHSA can converge; (2) its parameter setting does not depend on algorithm learning or prior knowledge; and (3) MHSA can describe complex behaviors and dynamics. The properties of MHSA, including correctness, convergence and stability, are discussed in details. Simulation results attest to the effectiveness and suitability of MHSA.