Abstract
Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.
Highlights
Many challenging problems in modern engineering and business domains challenge the design of satisfactory algorithms
We focus our discussion on the use of the scatter search and path-relinking (SS/Path Relinking (PR)) template [17] which we found very effective in creating benefits for marrying with evolutionary-based metaheuristics
We found that the glowworm swarm optimization (GSO) performs better than the firefly algorithm (FA) after 80,000 function evaluations (FE), the GSO may not beat the FA at the early stage of the execution as previously noted
Summary
Many challenging problems in modern engineering and business domains challenge the design of satisfactory algorithms. Researchers resort to either mathematical programming approaches or heuristic algorithms. Mathematical programming approaches are plagued by the curse of problem size and the heuristic algorithms have no guarantees to near-optimal solutions. Metaheuristic approaches have come as an alternative between the two extreme approaches. The metaheuristic approaches can be classified into two classes, evolution-based and memory-based algorithms. The evolutionary algorithms (EAs) iteratively improve solution quality by decent operations inspired by nature metaphors, creating several novel algorithms, such as genetic algorithms, artificial immune systems, ant colony optimization, and particle swarm optimization
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