Evolutionary optimisation algorithms

Pishchalnikov R.Y.

Prokhorov General Physics Institute of the Russian Academy of Sciences, 119991, Vavilova 38, Moscow, Russia

Optimization problems are ubiquitous in science and technology. Of course, many engineers and scientists need a reliable optimization algorithm to solve current computational problems that are the basis of their daily work. Ideally, an effective global optimization algorithm should not only be easy to use, but also powerful enough to reliably converge to the global optimum. In addition, the running time of the program spent on finding a solution should not be obscenely long. Thus, a truly useful and effective method of global optimization should be easy to implement, convenient to use, as well as reliable and fast.

Evolutionary optimization methods and genetic algorithms have been actively developed since about the sixties of the last century. Both approaches are aimed at developing better solutions through recombination, mutation and survival of the fittest, in fact imitating Darwinian evolution. There are some fundamental differences between evolutionary and genetic algorithms. For example, evolutionary algorithms are efficient optimizers of continuous functions, in particular because it encodes parameters as floating-point numbers and manipulates them using arithmetic operators. Genetic algorithms, on the contrary, are often better suited for combinatorial optimization, since they encode parameters in the form of bit strings and change them using logical operators.

The differential evolution algorithm has earned a reputation as a very effective evolutionary global optimizer since its introduction (1995). The algorithm is based on the method of selecting a trial mutant vector of optimized parameters: to get a trial vector, the algorithm adds a scaled difference of random vectors to a third, randomly selected population vector.

In our laboratory, differential evolution was actively used to model the optical response of photosynthetic pigments and proteins [1,2].


1. Pishchalnikov, R. Application of the differential evolution for simulation of the linear optical response of photosynthetic pigments // Journal of Computational Physics том 372, 2018, Стр. 603-615, doi:10.1016/

2. Chesalin, D.D.; Kulikov, E.A.; Yaroshevich, I.A.; Maksimov, E.G.; Selishcheva, A.A.; Pishchalnikov, R.Y. Differential evolution reveals the effect of polar and nonpolar solvents on carotenoids: A case study of astaxanthin optical response modeling // Swarm and Evolutionary Computation, 75, 2022, 101210, doi: 10.1016/j.swevo.2022.101210.


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