Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Abstract

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.

Optimally Configured Generative Adversarial Networks to Distinguish Real and AI- Generated Human Faces

“…The simulations showed LOA's strong exploration, exploitation, and balancing capabilities, outperforming twelve popular metaheuristic algorithms in problem-solving. This innovative approach has the potential to improve optimization in practical situations Dehghani et al (2023).…”

The Pine Cone Optimization Algorithm (PCOA)

“…The classic benchmark functions comprise seven unimodal (1 to 7), six multi-modal (8 to 13), and ten multi-modal fixed-dimension (14 to 23) functions. The CEC2017 test suite includes two unimodal (1 and 3), seven multi-modal (4-10), ten hybrid (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), and ten composite (21)(22)(23)(24)(25)(26)(27)(28)(29)(30) benchmark functions. Because of the unstable behavior, the second CEC2017 benchmark function was removed.…”

“…Moving swarms of birds or fish inspire the search processing in PSO. Researchers introduced the Artificial Bee Colony (ABC) in 2007 [10], Firefly Algorithm (FA) [11] in 2008, Bat Optimization Algorithm (BA) in 2012 [12], Krill-Herd Algorithm (KHA) in 2012 [13], Gray Wolf Optimizer (GWO) in 2014 [14], Whale Optimization Algorithm (WOA) in 2016 [15], Harris Hawks Optimization Algorithm [16] in 2019, Coati Optimization Algorithm (COA) in 2023 [17], Dung Beetle Optimizer (DBO) in 2023 [18], Kookaburra Optimization Algorithm (KOA) in 2023 [19], Giant Armadillo Optimization (GAO) in 2023 [20], Lyrebird Optimization Algorithm (LOA) in 2023 [21], and Humboldt Squid Optimization Algorithm (HSOA) in 2023 [22]. In this category, algorithms such as PSO have poor local optimization ability.…”

Stochastic Shaking Algorithm: A New Swarm-Based Metaheuristic and Its Implementation in Economic Load Dispatch Problem

“…Many of these swarmbased metaheuristics draw inspiration from animal behavior, adopting metaphors in their designs. Examples include the Kookaburra Optimization Algorithm (KOA) [10], Lyrebird Optimization Algorithm (LOA) [11], Stochastic Komodo Algorithm (SKA) [12], Green Anaconda Optimization (GAO) [13], Walrus Optimization Algorithm (WaOA) [14], Coati Optimization Algorithm (COA) [15], White Shark Algorithm (WSA) [16], Squirrel Search Optimization (SSO) [17], Tasmanian Devil Optimization (TDO) [18], Northern Goshawk Optimization (NGO) [19], Osprey Optimization Algorithm (OOA) [20], and others. Some swarm-based metaheuristics incorporate social behavior elements, as seen in the Migration Algorithm (MA) [21], Language Education Optimization (LEO) [22], Mother…”