Developmental Learning Perspective of Swarm Intelligence Algorithms
Swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.
Swarm intelligence algorithm develops its learning capacity that can better solve an optimization problem which is unknown at the algorithm’s design or implementation time.
In the swarm intelligence research field, we are facing to solve different types of optimization problems under different environments. For example, there are single objective optimization problems, multi-objective optimization problems, constrained optimization problems, combinational optimization problems, etc.
There are optimization problems under fixed environment, dynamically changing environment, unknown environment, etc. As claimed in no-free-lunch theory .there is no single algorithm that will work the best for all different problems. That is to say, one algorithm can be better for one kind of problems, but may be worse for other kinds of problems. An ideal optimization algorithm should have the ability to change itself to have the suitable capacity to learn and solve the problem to be solved under its own environment, that is to say, it should be able to develop its own learning capacity or learning potential which has special connection with the problem and its environment, therefore, to enable the algorithm to better learn and solve the problem.
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International journal of swarm intelligence and evolutionary computation