Improved Fitness-Dependent Optimizer Algorithm

Abstract

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

Hybrid Sine Cosine and Fitness Dependent Optimizer for Global Optimization

“…The proposed SC-FDO also incorporates an improved fitness weight ( ) calculation to increase the convergence and quality of the solutions. The calculation of the improved fitness weight ( ) follows this formula [25].…”

“…The proposed SC-FDO was compared with six wellknown nature-inspired algorithms, namely FDO [6], IFDO [25], SCA [4], WOA [8], PSO [1], and BOA [10]. The test functions for the benchmark can be categorized into unimodal functions (BF1-BF7), multimodal functions (BF8-BF13), and composite functions (BF14-BF19), listed in Table II.…”

“…For each benchmark test function, all the algorithms were tested with 30 runs. In the work of [4][6] [25], a total of 30 search agents and a maximum number of 500 iterations were used in the experiments. Thus, the population size was fixed to 30, and the maximum number of iterations was 500.…”

“…Furthermore, Muhammed [25] developed an improved fitness-dependent optimizer (IFDO) algorithm based on alignment and cohesion strategy to update the scout bees' location. The introduction of a random weight factor ( ), alignment and cohesion features in the IFDO improved the convergence speed of the FDO, but the enhancement features increased the algorithm's space complexity and led to slower exploitations in some cases.…”

“…Although the FDO and FDO variants outperformed several optimization algorithms, in some cases, they encounter slow convergence, poor exploitation and exploration, and memory wastage as a result of inefficient memory allocation. ❖ Aperiodic antenna array designs [25] ❖ Pedestrian evacuation model [25] ❖ Multi-source interconnected power system in the restructured environment [26] ❖ Uses alignment and cohesion to update the scout bees' location. ❖ Perform weight factor ( ) randomization.…”