Sensitivity of Nature Inspired Meta-Heuristic Algorithms to Parameter Settings and Development of Algorithms Look-Up Table

Raihanatu Mohammed Hamid, Hamma Sabo, Fatsuma Jauro, Ali Muhammad Usman, Haruna Chiroma

Abstract


Numerous studies have been conducted on nature-inspired metaheuristics, with a particular focus on hybridization, performance evaluation, and their application to solving optimization problems. However, one persistent challenge is the laborious process of selecting optimal parameter settings for these algorithms, lacking a systematic approach for easily determining suitable values. In this research, we explore the sensitivity of nature-inspired metaheuristic algorithms to changes in their parameter settings. We aim to develop a convenient look-up table containing recommended parameter combinations for ten popular algorithms in this field. These algorithms include Artificial Bee Colony (ABC), Bat Algorithm, Cuckoo Search Optimization (CSO), Firefly Algorithm (FFA), Flower Pollination Algorithm (FPA), Genetic Algorithm (GA), Hybrid Bat Algorithm, Mothflame Optimization (MFO), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The look-up table presents various parameter combinations along with corresponding solution quality and convergence speed, specifically in relation to global solutions. Among these algorithms, FPA demonstrates superior convergence by achieving the global solution in seven benchmark problems. FFA, ABC, CSO, and PSO also produce highly favorable results, closely approaching the global solution. In terms of runtime efficiency, CSO outperforms the other algorithms. By developing this comprehensive look-up table for nature-inspired metaheuristic algorithms, we contribute a valuable resource to the existing literature. This table offers an additional suggested approach for researchers to easily select optimal combinations of parameter settings. We believe that our work will greatly benefit the research community by simplifying the process of choosing appropriate parameter values for these algorithms.

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References


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