Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

Abstract The “No Free Lunch” theorem states that, averaged over all optimization problems,
without re-sampling, all optimization algorithms perform equally well. Optimization, search …

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Differential Evolution (DE) is a state-of-the art global optimization technique. Considerable
research effort has been made to improve this algorithm and apply it to a variety of practical …

Intelligent optimization algorithms based on evolutionary and swarm principles have been
widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent …

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions
in a single simulation run has practical relevance to problem solving across many fields …

History matching is a typical inverse problem that adjusts the uncertainty parameters of the
reservoir numerical model with limited dynamic response data. In most situations, various …

In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is
necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice …

Differential evolution (DE) has been proven to be one of the most powerful global numerical
optimization algorithms in the evolutionary algorithm family. The core operator of DE is the …