Slime Mould Algorithm (SMA)
Slime mould algorithm (SMA) is a powerful population-based optimizer based on the oscillation mode of slime mould in nature. In April 2020, the paper of SMA published in the prestigious journal of Future Generation Computer Systems (FGCS).
Concepts and remarks on solving the maze
Simply speaking, the slime mould can establish the optimal path to connect food in a relatively optimal way based on a combination of positive-negative feedback. Slime mould can dynamically adjust their search patterns according to the quality of food provenienceMathematical model and steps of SMA
The following remarks can theoretically help us to know why the developed SMA can be useful in exploring or exploiting the feature space of a given optimization problem:
- The adaptive weight W enables the SMA to maintain a certain disturbance rate while guaranteeing fast convergence, thus avoiding optimal local trapping during fast convergence.
- Vibration parameter (vb) allows the individual position of slime mould to contract in a specific way, thus ensuring the efficiency of the early exploration and the accuracy of the later exploitation.
- The adequate utilization of individual fitness values allows SMA to make better decisions based on historical information.
- The location updating decision parameter p and three different location updating methods ensure better adaptability of the SMA in different search phases.
Source codes of SMA algorithm
- The Matlab codes of the SMA is now publicly available here
- The Java codes of the SMA is now publicly available here
- The Python codes of the SMA is now publicly available here
- TheWord office files of SMA section including the Pseudo-code is publicly available here
- The Latex files of SMA section including the Pseudo-code is publicly available here
- You can also check researchgate to find these files here
- A github project for SMA and related repository and wiki is available at here
- You can download the paper from here
- You can download the extended file of the published paper from here
- If you do not have any access to Sciencedirect, please drop Ali Asghar Heidari an e-mail here and he will send you the paper.
If you have any question regarding the proposed SMA or you need any help in codes of SMA or any assistant in modeling your problem or need any help in preparing your proposal and manuscript, please simply drop an email to author Dr. Ali Asghar Heidari e-mail here and he will help you online.
We will always be happy to cooperate with you if you have any new idea or proposal on the SMA algorithm. You can contact me or Dr. Ali Asghar Heidari. Let’s enjoy finding the optimal solutions to your real-world problems.
We will always be happy to cooperate with you if you have any new idea or proposal on the SMA algorithm. You can contact me or Dr. Ali Asghar Heidari. Let’s enjoy finding the optimal solutions to your real-world problems.