Multi-objective Flower Algorithm for Optimization

Abstract

Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.Comment: 2 figures. arXiv admin note: substantial text overlap with arXiv:1312.567

Nature–inspired metaheuristic algorithms to find near–OGR sequences for WDM channel allocation and their performance comparison

“…Flower Pollination Algorithm and its Modi ed form X. S. Yang et. al [71,81,82], inspired by the ow pollination process of owering plants, introduced a novel nature-inspired optimization algorithm called Flower pollination algorithm (FPA). For describing this novel metaheuristic algorithm, the authors in [71] use the following four idealized rules:…”

“…Generally, a weighted sum approach [74,81,82] is used to combine the multiple objective functions (here f and f ) into a single composite objective function f .…”

On the Use of Nature Inspired Metaheuristic in Computer Game

“…Finally, it is worth observing that basic KHA utilizes several other evolutionary operators such as mutation (23) and crossover (22) for swarm member modifications. In the present iteration of the paper, these were not applied.…”

“…Newer algorithms, have been recently introduced for this tasking. These are: the Krill Herd Algorithm [15], [16], [17], Animal Migration Optimization [18], Wolf Search Algorithm [19], The Dragonfly Algorithm [20], Monarch Butterfly Optimization [21] and the Flower Pollination Algorithm [22]. Such bio-inspired metaheuristic algorithms are able to tackle very hard combinatorial optimisation problems [11] as well as, they can be applied for solving optimization problems in continuous space [23].…”

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation

“…Proposed by Yang in 2012, FPA [9] is a population-based intelligent optimization algorithm that simulates flower pollination behavior in nature; although relatively new, it has been extensively researched over the past two years. Yang and Xingshi He used FPA to solve a multi-objective optimization problem in 2013 [10], and Osama Abdel-Raouf used an improved FPA to solve sudoku puzzles in 2014 [11]. Already in 2015 Prathiba has used FPA to solve different economic load dispatch problems [12].…”