Evolutionary Computation (EC) and Swarm Intelligence (SI) offer a variety of approaches to deal with high-complexity real-world problems. Yet, not all algorithms from these fields constitute a novel search strategy. The majority of them replicate the ideas introduced in the established ones, i.e., Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) [6]. Moreover, several recently introduced algorithms contain a center-bias operator, making them unsuitable for optimization tasks [3]. Moreover, several other structural biases have been detected in such algorithms [7].
However, some algorithms contain promising mechanisms that can be used to overcome known limitations observed in stochastic nature-inspired algorithms. A recent work’s findings [5] support this idea. The Mine Explosion mechanism (found in the Mine Blast Algorithm) and the Big Bang - Big Crunch mechanism (from the homonymous algorithm) could be incorporated into algorithms with high exploitation ability to enhance their performance through exploration. And there are potentially more such mechanisms that can be found in the various nature-inspired algorithms and have beneficial effects for other methods.
Furthermore, novel operators have been proposed to enhance the algorithms’ performance. For example, fitness-distance balance is a recent selection method that enables the proper determination of candidates with the highest potential to improve the search process [2]. Also, a new set of evolutionary operators was presented in [4].
This Workshop focuses on the research of mechanisms that could be used to modify the algorithmic process of EC and SI algorithms. The aim is to enhance the performance of existing nature-inspired algorithms and overcome well-known drawbacks, such as premature convergence and structural bias. We encourage submissions that study the prospects of existing mechanisms and provide theoretical background on mechanisms or operators explicitly designed for EC and SI algorithms.
This Workshop fully seconds the call-for-action of [1]. Therefore, submissions proposing new metaphor-based algorithms are not encouraged. Meanwhile, the studied mechanisms and operators must be described and investigated using the normal, standard optimization terminology.
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[1] Claus Aranha, Christian L Camacho Villalón, Felipe Campelo, Marco Dorigo, Rubén Ruiz, Marc Sevaux, Kenneth Sörensen, and Thomas Stützle. Metaphor-based metaheuristics, a call for action: the elephant in the room. Swarm Intelligence, 16(1):1–6, 2022.
[2] Hamdi Tolga Kahraman, Sefa Aras, and Eyüp Gedikli. Fitness-distance balance (fdb): a new selection method for meta-heuristic search algorithms. Knowledge-Based Systems, 190:105169, 2020.
[3] Jakub Kudela. A critical problem in benchmarking and analysis of evolutionary computation methods. Nature Machine Intelligence, 4(12):1238–1245, 2022.
[4] Bernardo Morales-Castaneda, Oscar Maciel-Castillo, Mario A Navarro, Itzel Aranguren, Arturo Valdivia, Alfonso Ramos-Michel, Diego Oliva, and Salvador Hinojosa. Handling stagnation through diversity analysis: A new set of operators for evolutionary algorithms. In 2022 IEEE Congress on Evolutionary Computation (CEC), pages 1–7. IEEE, 2022.
[5] Marios Thymianis and Alexandros Tzanetos. Is integration of mechanisms a way to enhance a nature-inspired algorithm? Natural Computing, pages 1–21, 2022.
[6] Alexandros Tzanetos. Does the field of nature-inspired computing contribute to achieving lifelike features? Artificial Life, pages 1–25.
[7] Diederick Vermetten, Bas van Stein, Fabio Caraffini, Leandro L Minku, and Anna V Kononova. Bias: a toolbox for benchmarking structural bias in the continuous domain. IEEE Transactions on Evolutionary Computation, 26(6):1380–1393, 2022