# Marine-Predators-Algorithm
This is the source code of Marine Predators Algorithm written in MATLAB.
Marine Predators Algorithm (MPA) is a nature-inspired optimization algorithm which follows the rules that naturally govern in optimal foraging strategy and encounter rate policy between predator and prey in marine ecosystems.
Lévy strategy is a widespread pattern among marine predators (e.g. sharks, tunas, marlines) when searching
for the food in a prey-sparse environment, but when it comes to foraging in a prey-abundant area, the pattern
is prevalently switched to Brownian motion [1]. The optimal encounter rate policy in biological interaction
between predator and prey is also dependent on the type of the movement that each of the predator/prey is
taking and the velocity ratio of prey to predator [2].
<p align="center">
<img src="https://github.com/afshinfaramarzi/Marine-Predators-Algorithm/blob/master/shark-Img.png" width="60%"/>
</p>
The following highlights summarize the governing policies for the optimal foraging and interactions and memories
in marine predators:
* Marine predators use Lévy strategy for the environment with a low concentration of prey while employing
Brownian movement for the areas with abundant prey;
* They showed the same percentages of Lévy and Brownian movement during their lifetime of traversing different habitats;
* Due to the environmental effects such as natural (eddy formation) or human-caused (FADs), they change their behavior to
hopefully find areas with a different distribution of prey;
* In low-velocity ratio (v=0.1), the best strategy for a predator is Lévy; either prey is moving in Brownian or Lévy;
* In the unit velocity ratio (v=1), if prey moves in Lévy, the best strategy for a predator is Brownian. Other scenarios are dependent on system size;
* In high-velocity ratio (v≥10) the best strategy for a predator is not moving at all. In this case, either prey is moving Brownian or Lévy;
* They take advantage of good memory in reminding of their associates as well as the location of successful foraging.
Based on the highlights, MPA optimization process is divided into three main phases of optimization considering different
velocity ratio and at the same time mimicking the entire life of a predator and prey: (1) in high velocity ratio or when
prey is moving faster than predator, (2) in unit velocity ratio or when both predator and prey are moving at almost same
pace, and (3) in low velocity ratio when predator is moving faster than prey.
<p align="center">
<img src="https://raw.githubusercontent.com/afshinfaramarzi/Marine-Predators-Algorithm/master/MPA-Phases.jpg" width="100%"/>
</p>
Besides the three phases defined for optimization, some natural and human-caused environmental issues such as eddy formation
and FAD’s effect are mathematically modeled to include in the MPA.
MPA’s performance is evaluated against twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape,
three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy
performance. MPA is compared with three classes of existing optimization methods, including 1) GA and PSO as the most
well-studied metaheuristics, 2) GSA, CS and SSA as almost recently developed algorithms and 3) CMA-ES, SHADE and
LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second
rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the
winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance
optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is
statistically similar to SHADE and LSHADE-cnEpSin.
You can download the main paper here: https://doi.org/10.1016/j.eswa.2020.113377
If you don’t have access to the paper, just leave me a message at afaramar@hawk.iit.edu or afshin.faramarzi@gmail.com and I will get back to you soon.
References:
[1] Humphries, N. E., Queiroz, N., Dyer, J. R. M., Pade, N. G., Musyl, M. K., Schaefer, K. M., Fuller, D. W., Brunnschweiler, J. M., Doyle, T. K., Houghton, J. D. R., Hays, G. C., Jones, C. S., Noble, L. R., Wearmouth, V. J., Southall, E. J., & Sims, D. W. (2010). Environmental context explains Lévy and Brownian movement patterns of marine predators. Nature, 465(7301), 1066.
[2] Bartumeus, F., Catalan, J., Fulco, U. L., Lyra, M. L., & Viswanathan, G. M. (2002). Optimizing the encounter rate in biological interactions: Lévy versus Brownian strategies. Physical Review Letters, 88(9), 097901.
MATLAB源码集锦-matlab多目标优化之海洋捕食者算法
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