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GaMeDE2 - improved Gap–based Memetic Differential Evolution applied to multi–modal optimisation

Antkiewicz Michał, Myszkowski Paweł B., Laszczyk Maciej

Annals of Computer Science and Information Systems

2022

Vol. 30

291--300

This paper presents an improved Gap-based Memetic Differential Evolution (GaMeDE2), the modification of the GaMeDE method, which took second place in the GECCO 2020 Competition on Niching Methods for Multi-modal Optimization. GaMeDE2 has reduced complexity, fewer parameters, redefined initialisation, selection operator, and removed processing phases. The method is verified using standard benchmark function sets (classic ones and CEC2013) and a newly proposed benchmark set comprised of deceptive functions. A detailed comparison to state-of-the-art methods (like HVCMO and SDLCSDE) is presented, where the proposed GaMeDE2 outperforms or gives similar results to other methods. The document is concluded by discussing various insights on the problem instances and the methods created as a part of the research.

Non-dominated sorting tournament genetic algorithm for multi-objective travelling salesman problem

Myszkowski Paweł B., Laszczyk Maciej, Dziadek Kamil

Annals of Computer Science and Information Systems

2019

Vol. 18

67--76

A Travelling Salesman Problem (TSP) is an NP-hard combinatorial problem that is very important for many real-world applications. In this paper, it is shown, that proposed approach solves multi-objective TSP (mTSP) more effectively than other investigated methods, i.e. Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed methods use rank and crowding distance (well-known from NSGA-II), combining those mechanisms in a novel, unique way: competing and co-evolving in the evolution process. The proposed modifications are investigated and verified by the benchmark mTSP instances, and results are compared to other methods.

A Specialized evolutionary approach to the bi-objective travelling thief problem

Laszczyk Maciej, Myszkowski Paweł B.

Annals of Computer Science and Information Systems

2019

Vol. 18

47--56

In the recent years, it has been shown that real world-problems are often comprised of two, interdependent subproblems. Often, solving them independently does not lead to the solution to the entire problem. In this article, a Travelling Thief Problem is considered, which combines a Travelling Salesman Problem with a Knapsack Problem. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is investigated, along with its recent modification - a Non-Dominated Tournament Genetic Algorithm (NTGA). Each method is investigated in two configurations. One, with generic representation, and genetic operators. The other, specialized to the given problem, to show how the specialization of genetic operators leads to better results. The impact of the modifications introduced by NTGA is verified. A set of Quality Measures is used to verify the convergence, and diversity of the resulting PF approximations, and efficiency of the method. A set of experiments is carried out. It is shown that both methods work almost the same when generic representation is used. However, NTGA outperforms classical NSGA-II in the specialized results.