![]() ![]() ![]() Simon drew parallels between BBO and the evolution of GA and artificial neural networks, particularly emphasizing its prowess in addressing optimization challenges. This approach taps into the mathematical principles underlying the habitat distribution of life forms. Similarly inspired by natural processes, Simon ( 2008) unveiled the biogeography-based optimization (BBO). Introduced by Holland ( 1992), the GA has since garnered extensive attention across various domains, serving as a go-to method for optimization and search problems. Among these, the genetic algorithm (GA) stands out as one of the most renowned. ![]() 1).Įvolutionary algorithms, a subset of meta-heuristic techniques, emulate the evolutionary mechanisms observed in nature. Additionally, they can be classed by their foundational inspiration, which could be grounded in swarm intelligence, evolutionary principles, physics, mathematics, or human concepts (Fig. In a broader context, metaheuristics are categorized according to the extent of random choices they engender during each optimization iteration. ![]() Another distinguishing feature of metaheuristics is their adaptability. This attribute ensures they are less susceptible to ensnarement in local optima compared to their traditional counterparts. In stark contrast to traditional methods that necessitate well-defined mathematical models, metaheuristics optimize by varying inputs and scrutinizing the corresponding outputs to maximize or minimize objective functions. 2011) that operate by generating and refining random solutions iteratively. Various domains have leveraged these methods (Boussaïd et al. Given the pivotal role of optimization across diverse fields, there has been an upswing in the emphasis on metaheuristic techniques (Spall 2005). Their inherent robustness, coupled with domain independence, renders them indispensable in a myriad of practical applications. One of the salient characteristics of meta-heuristics is their relative indifference to initial conditions, which reinforces the consistency of optimization outcomes. This design feature significantly augments the likelihood of pinpointing the global optimum in an array of optimization contexts. However, their performance can be less than satisfactory in scenarios dominated by multi-extremum challenges.Ĭonversely, meta-heuristic algorithms, through their astute design, manage to strike a nuanced balance between evading local optima and ensuring convergence to a viable solution. They are particularly adept at addressing problems characterized by a single extremum. These traditional methods are amenable to rigorous evaluation through computational complexity and convergence theories. Such problems possess clear structural information, established parameters, and a uniquely identifiable global optimum. Traditional optimization techniques, such as integer programming (Williams 2009), linear programming (Dantzig 2002), mixed programming (Hooker and Osorio 1999), and various constrained optimization methods (Gautier and Granot 1994), are typically designed for well-structured problems. Meta-heuristic algorithms, when compared to traditional optimization methods, distinguish themselves by their simplicity in terms of both comprehension and implementation. ![]()
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