Migration-Crossover Algorithm: A Swarm-based Metaheuristic Enriched with Crossover Technique and Unbalanced Neighbourhood Search

Abstract

There has been a massive development of metaheuristic algorithms in the latest decade where swarm intelligence becomes the fundamental approach. Meanwhile, there is still no ideal metaheuristic that can solve all problems superiorly, as declared in the no-free-lunch (NFL) theory. This work introduces a novel swarm-based metaheuristic named as migration-crossover algorithm (MCA). In MCA, the swarm intelligence is enriched with the crossover technique and the neighbourhood search with unbalanced local search space. The global finest solution becomes the reference in the first step while the middle between two stochastically chosen solutions becomes the reference in the second step. The neighbourhood search is performed in the third step. The collection of 23 functions become the use case during the evaluation of MCA. In the first evaluation, MCA is compared with five new metaheuristics: total interaction algorithm (TIA), osprey optimization algorithm (OOA), migration algorithm (MA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA). The result reveals that MCA is finer than TIA, OOA, MA, COA, and WaOA in 20,19,17,20,and 17 functions subsequently. The result of the second evaluation reveals that the global finest solution becomes the dominant contributor in the high dimension functions while the middle between two stochastically chosen solutions becomes the dominant contributor in the fixed dimension functions.

Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems

“…ABC simulates hierarchical behaviors within bee colonies, and FA takes inspiration from the Information exchange through optical communication among fireflies. Natural behaviors among living organisms such as foraging, hunting, migration, digging, flight strategy, and chasing process have been sources of inspiration in designing swarm-based algorithms such as: Pufferfish Optimization Algorithm [16], Grey Wolf Optimizer (GWO) [17], Wombat Optimization Algorithm (WOA) [18], Termite Alate Optimization Algorithm (TAOA) [19], Whale Optimization Algorithm (WOA) [20], African Vultures Optimization Algorithm (AVOA) [21], Swarm Space Hopping Algorithm (SSHA) [22], Reptile Search Algorithm (RSA) [23], Marine Predator Algorithm (MPA) [24], Migration-Crossover Algorithm (MCA) [25], White Shark Optimizer (WSO) [26], and Tunicate Swarm Algorithm (TSA) [27].…”

Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

“…International Journal of Intelligent Engineering and Systems, Vol.17, No.2, 2024 DOI: 10.22266/ijies2024.0430. 35 EDS emerges as a valuable tool to manage the influx of renewable energy and EVs by optimizing the distribution system's topology. It minimizes power losses, improves voltage profiles, and enhances grid flexibility to accommodate the varying nature of RESs and EV charging demands.…”

“…While INGO has demonstrated its effectiveness, there remains a need to assess its performance in comparison to emerging state-of-the-art metaheuristics. These include the four directed search algorithm (FDSA) [31], walk-spread algorithm (WSA) [32], attack leave optimizer (ALO) [33], total interaction algorithm (TIA) [34], and migration-crossover algorithm (MCA) [35]. This serves as an extension of our ongoing research work.…”