In recent years, the field of combinatorial optimization has witnessed a true tsunami of so-called "novel" metaheuristic methods, most of them based on a metaphor of some natural or man-made process. The behavior of virtually any species of insects, the flow of water, musicians playing together -- it seems that no idea is too far-fetched to serve as an inspiration to launch yet another metaheuristic.
Such methods are being developed for better performance by researchers in many disciplines of science and engineering.
I found it interesting..
Metaheuristic Optimization Algorithm
https://www.sciencedirect.com/topics/computer-science/metaheuristic-optimization-algorithm
The nature based algorithms are particular cases of making evolve codes algorithms, called in some my own works with the general denomination of the Integration of Variables method. I think such kind of algorithms will appear a great quantity in a next future.
The following article discusses in detail this aspect and the rationality that needs to be adopted while developing nature-based metaheuristic algorithms.
Article Nature inspired optimization algorithms or simply variations...
I would like to invite you to have a look on the following links.
SSO: https://www.mathworks.com/matlabcentral/fileexchange/92150-sperm-swarm-optimization-sso
HSSOGSA: https://www.mathworks.com/matlabcentral/fileexchange/92130-hssogsa?s_tid=srchtitle
MOSFP: https://github.com/sh7adeh1990/MOSFP
Well, meta-heuristics were originally conceived due to a desire to create a problem-independent, all encompassing global optimization algorithm. Conventional mathematical programming techniques, although more reliable in their optimized solutions, were problem specific, i.e., one method worked for one type of problem and not the other.
The field seems to be currently fixated on achieving greater computational efficiency by means of better balancing "exploration to exploitation.". The issue now is to find the proper combination between these two search schemes for every problem. I think the next step in meta-heuristics is for it to combine with "reinforcement learning" and create an intelligently adaptive algorithm that modifies its search behavior based on the problem at hand. Algorithms will be trained for optimal performance instead of created from scratch.
Chapter Metaheuristic Optimization Algorithms
Article Metaheuristic methods in combinatorial optimization
A metaheuristic method helps in solving the optimization problem. Problems in optimization can be found in many daily life aspects. The kinds of the metaheuristic method are various which are ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO).
An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till an optimum or a satisfactory solution is found. With the advent of computers, optimization has become a part of computer-aided design activities.
- Badges
- Science topic