Swarm Intelligence for Multiobjective Optimization of Extraction Process

Multi objective (MO) optimization is an emerging field which is increasingly being implemented in many industries globally. In this work, the MO optimization of the extraction process of bioactive compounds from the Gardenia Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been applied in conjunction with normal-boundary intersection (NBI) method to solve this MO problem. The gravitational search algorithm (GSA) and the particle swarm optimization (PSO) technique were implemented in this work. In addition, a novel Hopfield-enhanced particle swarm optimization was developed and applied to the extraction problem. By measuring the levels of dominance, the optimality of the approximate Pareto frontiers produced by all the algorithms were gauged and compared. Besides, by measuring the levels of convergence of the frontier, some understanding regarding the structure of the objective space in terms of its relation to the level of frontier dominance is uncovered. Detail comparative studies were conducted on all the algorithms employed and developed in this work.

[1]  Kusum Deep,et al.  Multi Objective Extraction Optimization of Bioactive Compounds from Gardenia Using Real Coded Genetic Algorithm , 2010 .

[2]  Sang Heon Lee,et al.  A faster path planner using accelerated particle swarm optimization , 2012, Artificial Life and Robotics.

[3]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[4]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[5]  June Ho Park,et al.  Adaptive Hopfield neural networks for economic load dispatch , 1998 .

[6]  C. Ho,et al.  Optimization of extraction conditions for phenolic compounds from neem (Azadirachta indica) leaves , 2011 .

[7]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[8]  Choong Seon Hong,et al.  A Fast Algorithm to Calculate Powers of a Boolean Matrix for Diameter Computation of Random Graphs , 2008, WALCOM.

[9]  Freeman J. Dyson,et al.  Existence of a phase-transition in a one-dimensional Ising ferromagnet , 1969 .

[10]  Ajith Abraham,et al.  Swarm Intelligence: Foundations, Perspectives and Applications , 2006, Swarm Intelligent Systems.

[11]  Farrokh Mistree,et al.  THE COMPROMISE DECISION SUPPORT PROBLEM AND THE ADAPTIVE LINEAR PROGRAMMING ALGORITHM , 1998 .

[12]  Roman B. Statnikov,et al.  Multicriteria Optimization and Engineering , 1995 .

[13]  Pandian Vasant,et al.  Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution , 2015, Appl. Soft Comput..

[14]  I. Elamvazuthi,et al.  Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production , 2013 .

[15]  Peter C. Fishburn,et al.  Letter to the Editor - Additive Utilities with Incomplete Product Sets: Application to Priorities and Assignments , 1967, Oper. Res..

[16]  Adel Nadjaran Toosi,et al.  Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications , 2012, Artificial Intelligence Review.

[17]  Andrew Kusiak,et al.  Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm , 2011 .

[18]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[19]  Payam Parvasi,et al.  Dynamic optimization of a novel radial-flow, spherical-bed methanol synthesis reactor in the presence of catalyst deactivation using Differential Evolution (DE) algorithm , 2009 .

[20]  Carlos A. Coello Coello,et al.  Evolutionary multiobjective design targeting a Field Programmable Transistor Array , 2004, Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004..

[21]  Kalyanmoy Deb,et al.  Multi-objective Performance Optimization of Thermo-Electric Coolers Using Dimensional Structural Parameters , 2010, SEMCCO.

[22]  M. Janga Reddy,et al.  An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design , 2007 .

[23]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Pandian Vasant,et al.  Iterative Fuzzy Optimization Approach for Crude Oil Refinery Industry , 2010 .

[25]  M. Bissonnette,et al.  Spectroscopic Characterization of Crocetin Derivatives from Crocus sativus and Gardenia jasminoides , 1997 .

[26]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[27]  Beom-Seon Jang,et al.  Managing approximation models in multiobjective optimization , 2003 .

[28]  Evangelos Triantaphyllou,et al.  Multi-Criteria Decision Making Methods , 2000 .

[29]  T. Ganesan,et al.  An Algorithmic Framework for Multiobjective Optimization , 2013, TheScientificWorldJournal.

[30]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[31]  Patrick McCluskey,et al.  An Interactive Multistage ε-Inequality Constraint Method For Multiple Objectives Decision Making , 1998 .

[32]  Kalyanmoy Deb,et al.  Mechanical Component Design for Multiple Objectives Using Elitist Non-dominated Sorting GA , 2000, PPSN.

[33]  Kalyanmoy Deb,et al.  Running performance metrics for evolutionary multi-objective optimizations , 2002 .

[34]  Jianzhong Zhou,et al.  Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm , 2011 .

[35]  Swati Mohanty,et al.  Multiobjective optimization of synthesis gas production using non-dominated sorting genetic algorithm , 2006, Comput. Chem. Eng..

[36]  I. Zelinka,et al.  Creating Evolutionary Algorithms By Means Of Analytic Programming – Design Of New Cost Function , 2007 .

[37]  Kalaimany Arumuggam,et al.  Optimization of Hybrid Solar and Wind Power Generation , 2013 .

[38]  David A. Cartes,et al.  Intelligent power management in micro grids with EV penetration , 2013, Expert Syst. Appl..

[39]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[40]  Jonas Krause,et al.  A Survey of Swarm Algorithms Applied to Discrete Optimization Problems , 2013 .

[41]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[42]  T. Ganesana,et al.  Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment , 2014 .

[43]  R. Garduno-Ramirez,et al.  Multiobjective control of power plants using particle swarm optimization techniques , 2006, IEEE Transactions on Energy Conversion.

[44]  S. A. Raut,et al.  Optimization of Continuous Extraction Column and Solvent Selection Using Differential Evolution Technique , 2012 .

[45]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[46]  T. Ganesan,et al.  A comparative study of HNN and Hybrid HNN-PSO techniques in the optimization of distributed generation (DG) power systems , 2011, 2011 International Conference on Advanced Computer Science and Information Systems.

[47]  Mazidah Puteh,et al.  An overview of Gravitational Search Algorithm utilization in optimization problems , 2013, 2013 IEEE 3rd International Conference on System Engineering and Technology.

[48]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[49]  John J. Hopfield,et al.  Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

[50]  Y H Chen,et al.  Determination of geniposide, gardenoside, geniposidic acid and genipin-1-beta-gentiobioside in Gardenia jasminoides by high-performance liquid chromatography. , 1988, Journal of chromatography.

[51]  Christodoulos A. Floudas,et al.  Analyzing the interaction of design and control—1. A multiobjective framework and application to binary distillation synthesis , 1994 .

[52]  Feng Chen,et al.  Antioxidant properties in vitro and total phenolic contents in methanol extracts from medicinal plants , 2008 .

[53]  Bart Baesens,et al.  Editorial survey: swarm intelligence for data mining , 2010, Machine Learning.

[54]  Oguzhan Ceylan,et al.  Branch outage simulation based contingency screening by gravitational search algorithm , 2012 .

[55]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[56]  Nicola Beume,et al.  An EMO Algorithm Using the Hypervolume Measure as Selection Criterion , 2005, EMO.

[57]  Chen Wei,et al.  Optimization of extraction process of crude polysaccharides from wild edible BaChu mushroom by response surface methodology , 2008 .