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The humanoid ant algorithm (HUMANT) is an ant colony optimization algorithm. The algorithm is based on a priori approach to multi-objective optimization (MOO), which means that it integrates decision-makers preferences into optimization process. Using decision-makers preferences, it actually turns multi-objective problem into single-objective. It is a process called scalarization of a multi-objective problem. The first multi-objective ant colony optimization (MOACO) algorithm was published in 2001, but it was based on a posteriori approach to MOO.

The idea of using the preference ranking organization method for enrichment evaluation to integrate decision-makers preferences into MOACO algorithm was born in 2009. HUMANT is the only known fully operational optimization algorithm that successfully integrates PROMETHEE method into ACO.

The HUMANT algorithm has been experimentally tested on the traveling salesman problem and applied to the partner selection problem with up to four objectives (criteria).

References

1. Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2015). "Single-Objective and Multi-Objective Optimization using the HUMANT algorithm". Croatian Operational Research Review. 6 (2): 459–473. doi:10.17535/crorr.2015.0035.
2. Talbi, El-Ghazali (2009). Metaheuristics – From Design to Implementation. John Wiley & Sons.
3. Eppe, Stefan (2009). "Application of the Ant Colony Optimization Metaheuristic to multi-objective optimization problems". Technical Report – ULB, Bruxelles.
4. Iredi, Steffen; Merkle, Daniel; Middendorf, Martin (2001). "Bi-Criterion Optimization with Multi Colony Ant Algorithms". Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science. 1993: 359–372. doi:10.1007/3-540-44719-9_25. ISBN 978-3-540-41745-3.
5. Eppe, Stefan (2009). "Integrating the decision maker's preferences into Multi Objective Ant Colony Optimization". Proceedings of the 2nd Doctoral Symposium on.
6. Al-Janabi, Rana JumaaSarih; Al-Jubouri, Ali Najam Mahawash (2022), "Multi-key Encryption Based on RSA and Block Segmentation", Biologically Inspired Techniques in Many Criteria Decision Making, Singapore: Springer Nature Singapore, pp. 687–695, ISBN 978-981-16-8738-9, retrieved 2023-11-03
7. Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2017). "Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm". International Journal of Production Research. 55 (9): 2506–2521. doi:10.1080/00207543.2016.1234084.

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v t e Optimization: Algorithms, methods, and heuristics
Unconstrained nonlinear Functions Golden-section search Powell's method Line search Nelder–Mead method Successive parabolic interpolation Gradients Convergence Trust region Wolfe conditions Quasi–Newton Berndt–Hall–Hall–Hausman Broyden–Fletcher–Goldfarb–Shanno and L-BFGS Davidon–Fletcher–Powell Symmetric rank-one (SR1) Other methods Collinear gradients method Conjugate gradient Gauss–Newton Gradient Mirror Levenberg–Marquardt Powell's dog leg method Truncated Newton Hessians Newton's method
Constrained nonlinear General Barrier methods Penalty methods Differentiable Augmented Lagrangian methods Sequential quadratic programming Successive linear programming
Convex optimization Convex minimization Cutting-plane method Reduced gradient (Frank–Wolfe) Subgradient method Linear and quadratic Interior point Affine scaling Ellipsoid algorithm of Khachiyan Projective algorithm of Karmarkar Basis-exchange Simplex algorithm of Dantzig Revised simplex algorithm Criss-cross algorithm Principal pivoting algorithm of Lemke Active-set method
Combinatorial Paradigms Approximation algorithm Dynamic programming Greedy algorithm Integer programming Branch and bound/cut Graph algorithms Minimum spanning tree Borůvka Prim Kruskal Shortest path Bellman–Ford SPFA Dijkstra Floyd–Warshall Network flows Dinic Edmonds–Karp Ford–Fulkerson Push–relabel maximum flow
Metaheuristics Evolutionary algorithm Hill climbing Local search Parallel metaheuristics Simulated annealing Spiral optimization algorithm Tabu search
Software

• Nature-inspired metaheuristics