Flower pollination algorithm parameters tuning

Abstract

The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC’13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost.

High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics

“…A flower-based algorithm, known as flower pollination algorithm (FPA), can be designed from the above principles and arguments. Global pollination and local pollination are two major stages of this algorithm [35]. In the phase of global pollination, insects work as pollinators for carrying flower pollens over long distances because of their capability to move and fly for a more extended range.…”

An Aggressive Cuckoo Search Algorithm for Optimum Power Allocation in a CDMA-Based Cellular Network

“…The ACSA consistently had around the fifth or sixth shortest execution time among the 10 standard optimization algorithms. Although, this is due to the algorithm being compared to consistently fast algorithms such as the flower pollination algorithm which is characterized by being simple in its formulation and thus having a high computational performance [ 36 ].…”

Robust Modified Flower Pollination Algorithm for Power Quality Enhancement in an Autonomous 31-Level Cascaded H-Bridge Photovoltaic Inverter with Partial Shading Conditions

“…However, nature-inspired optimization techniques can automatically respond to system parameters changes without physically changing the control parameters. In the recent past, there are many evolutionary techniques available in the literature as suggested by many authors, such as Particle Swarm Optimization (PSO) [16], Weighted Superposition Attraction Algorithm (WSAA) [17], Bee Colony Optimization (BCO) [18], Brain Storming Optimization (BSO) [19], Spider Monkey Algorithm (SMA) [20], Crow Search (CS) [21], Flower Pollination Algorithm (FPA) [22], etc.…”

“…Flower's essential function is to spawn through the act of pollination. Pollination occurs when the male anther conveys its pollen grains to the female stigma of a flower of the alike or different plant [22]. Pollinators such as insects, birds are responsible for this transfer.…”