Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

Abstract

In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.

An Enhanced Slime Mould Optimizer That Uses Chaotic Behavior and an Elitist Group for Solving Engineering Problems

“…for the minimum and maximum, respectively. To minimize the fuel costs case, the proposed ESMO, standard SMO, and other recent versions of the SMO of LSMO [31], EQSMO [32], AOSMO [33], and FDBSMO [34] were performed. Table 6 describes the parameter settings of each applied algorithm to solve the OPF issue.…”

“…Table 6 describes the parameter settings of each applied algorithm to solve the OPF issue. As shown, the same number of function evaluations was maintained at 15,000 To minimize the fuel costs case, the proposed ESMO, standard SMO, and other recent versions of the SMO of LSMO [31], EQSMO [32], AOSMO [33], and FDBSMO [34] were performed. Table 6 describes the parameter settings of each applied algorithm to solve the OPF issue.…”

“…These considerations guarantee a fair comparison with equivalent fitness functions between the applied methods. The attained outputs of the proposed ESMO and other recent versions of the SMO, i.e., LSMO [31], EQSMO [32], AOSMO [33], and FDBSMO [34], are displayed in Table 7. In addition, Figure 5 In particular, after utilizing the suggested ESMO and other SMO versions, Figure 6 depicts the box plot of the thirty obtained fitnesses of the derived fuel costs.…”

“…It has been used to address various real engineering optimizing issues because of its high globally searching ability and resilience, such as economic emission dispatch [26], optimal power flow [27], operation of cascade hydropower stations [28], demand estimation of urban water resources [29], and design optimization problems [30]. Added to that, other recent versions of the SMO were effectively presented, such as the leader SMO (LSMO) [31], equilibrium SMO (EQSMO) [32], adaptive opposition SMO (AOSMO) [33], and fitness-distance-balance SMO (FDBSMO) [34]. Additionally, different statistical comparisons of the competing meta-heuristic optimization were reviewed [35,36], where numerous runs of different optimizers can be effectively compared.…”

Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer

“…Fitness-distance balance (FDB) is a common selection method that has been applied for improving the performance of optimization algorithms [27] like the stochastic fractal search algorithm [28], differential evolution [29], moth swarm algorithm [30], particle swarm optimizer [31], wind-driven optimization [32] and coyote optimization [33]. Improving the searching ability of the RUN technique using FDB was presented in [34,35].…”