Exploiting co-existence and co-evolution of mutualistic communities: A stable algorithm based on the plant-pollinator interactions. (February 2019)

• Record Type: Journal Article
• Authors: Khan, Mohammad Haris Ali; Jain, Richa; Thakur, Laxman; Kumar, Sri Krishna; Tiwari, Manoj Kumar
• Is Part Of: Computers & industrial engineering. Volume 128(2019)
• Page Start: 637
• Page End: 650
• Publication Date: 2019-02
• Subjects: Biological systems -- Bio-inspired algorithms -- Continuous and discrete optimization -- Stochastic process; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.12.060
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: New optimization algorithm with novel stochastic search approach mimicking features of evolution. Proposed approach and its variants tested against 14 benchmark problems. The results are compared with that of hybrids of Particle Swarm Optimization. The implementation of PPO on a realistic discrete optimization problem demonstrated. Abstract: In this paper, we have build-up a new optimization algorithm with novel stochastic search approach mimicking features of evolution and mutualistic cooperation observed among plants and animal pollinators in nature. The framework, based on the mathematics of the optimization mechanism of the plant-pollinator dynamics inspired the metaphorical design of the search strategies. We name our algorithm as a Plant pollinator optimization (PPO) and propose its variants by adding a few purposeful features of Evolutionary Algorithms. The competitive performance of the proposed approach and its variants is tested against 14 benchmark problems and superior performance of the proposed approach has been reported. The results are compared with that of hybrids of Particle Swarm Optimization adopted from literature. Subsequently, the results are used to draw statistical inferences regarding the convergence characteristics, exploratory features and consistency of the algorithm. The implementation of PPO on a realistic discrete optimization problem of dynamic scheduling and chartering of oil tankers further demonstrates the suitability of the proposed algorithm.