A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
On mass effects to artificial physics optimisation algorithm for global optimisation problems
International Journal of Innovative Computing and Applications
The vector model of artificial physics optimization algorithm for global optimization problems
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Artificial physics optimisation: a brief survey
International Journal of Bio-Inspired Computation
A Hybrid Vector Artificial Physics Optimization with One-Dimensional Search Method
CASON '10 Proceedings of the 2010 International Conference on Computational Aspects of Social Networks
Linear antenna array synthesis with modified invasive weed optimisation algorithm
International Journal of Bio-Inspired Computation
Wisdom of artificial crowds algorithm for solving NP-hard problems
International Journal of Bio-Inspired Computation
The convergence analysis of artificial physics optimisation algorithm
International Journal of Intelligent Information and Database Systems
International Journal of Computer Applications in Technology
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A Hybrid Particle Swarm Branch-and-Bound (HPB) Optimizer for Mixed Discrete Nonlinear Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
International Journal of Computer Applications in Technology
Multi-agent simulated annealing algorithm based on particle swarm optimisation algorithm
International Journal of Computer Applications in Technology
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Artificial physics optimisation APO is a novel population-based stochastic algorithm inspired by physicomimetics. APO with the feasibility and dominance method EAD-APO is employed to solve constrained optimisation problems. In EAD-APO, the mass of each feasible individual corresponds to a user-defined function of the value of an objective to be optimised, and the mass of each infeasible individual corresponds to a user-defined function of the constraint violation value, which can supply some important information for searching global optima. There are many functions can be used as mass function, and no doubt some will be better than others for specific optimisation problems or perhaps classes of problems. This paper proposes the basic regulation and design method of mass function, and classifies mass functions into three different types of curvilinear functions according to their curvilinear styles, such as linear function, convex function, and concave function. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations.