Development and application of slime mould algorithm for optimal economic emission dispatch

Abstract In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch (EED) problems considering valve point effect. ISMA is developed to improve the performance of the conventional Slime Mould Algorithm (SMA). In ISMA, the solution positions are updated depending on two equations borrowed from the sine–cosine algorithm (SCA) to obtain the best solution. Multi-objective SMA (MOSMA) and Multi-objective ISMA (MOISMA) are developed based on the Pareto dominance concept and fuzzy decision-making. In the multi-objective EED problem, MOSMA and MOISMA are applied to minimize the total fuel costs and total emission with the valve point effect simultaneously. The proposed single-and bi-objective economic emission dispatch algorithms are validated using five test systems, 6-units, 10-units, 11-units, 40-units, and 110-units. The performance of the proposed algorithm is compared with Harris Hawk Optimizer (HHO), Jellyfish Search optimizer (JS), Tunicate Swarm Algorithm (TSA), Particle swarm optimization (PSO), and SMA algorithms. The results show that the proposed algorithms are more robust than other well-known algorithms. Feasible solutions using the proposed algorithms are also achieved, which adjust the schedule of generation without violation of the operating generation limits.

[1]  Esmaile Khorram,et al.  Two efficient algorithms for constructing almost even approximations of the Pareto front in multi-objective optimization problems , 2018, Engineering Optimization.

[2]  A. Salleo,et al.  Multi-phase microstructures drive exciton dissociation in neat semicrystalline polymeric semiconductors , 2015 .

[3]  Chao-Lung Chiang,et al.  Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels , 2005 .

[4]  Salah Kamel,et al.  Single- and multi-objective optimal power flow frameworks using Jaya optimization technique , 2019, Neural Computing and Applications.

[5]  J. José,et al.  Nonspherical armoured bubble vibration. , 2017, Soft matter.

[6]  M. Balasingh Moses,et al.  Flower Pollination Algorithm Applied for Different Economic Load Dispatch Problems , 2014 .

[7]  Carolina P. de Almeida,et al.  An empirical analysis of constraint handling on evolutionary multi-objective algorithms for the Environmental/Economic Load Dispatch problem , 2021, Expert Syst. Appl..

[8]  Huiling Chen,et al.  Slime mould algorithm: A new method for stochastic optimization , 2020, Future Gener. Comput. Syst..

[9]  James M. Calvin,et al.  On efficiency of a single variable bi-objective optimization algorithm , 2020, Optim. Lett..

[10]  L. Coelho,et al.  Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect , 2006, IEEE Transactions on Power Systems.

[11]  Amir H. Gandomi,et al.  Marine Predators Algorithm: A nature-inspired metaheuristic , 2020, Expert Syst. Appl..

[12]  Amir Hossein Alavi,et al.  A comprehensive review of krill herd algorithm: variants, hybrids and applications , 2017, Artificial Intelligence Review.

[13]  Sam Devlin,et al.  Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract) , 2016, AAMAS.

[14]  Malcolm Irving,et al.  Large scale unit commitment using a hybrid genetic algorithm , 1997 .

[15]  A. Ebenezer Jeyakumar,et al.  Hybrid PSO–SQP for economic dispatch with valve-point effect , 2004 .

[16]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[17]  V. P. Sakthivel,et al.  Coulomb’s and Franklin’s Laws Based Optimization for Nonconvex Economic and Emission Dispatch Problems , 2020 .

[18]  Dexuan Zou,et al.  A new global particle swarm optimization for the economic emission dispatch with or without transmission losses , 2017 .

[19]  Mehrdad Tarafdar Hagh,et al.  Solution to economic emission dispatch problem including wind farms using Exchange Market Algorithm Method , 2020, Appl. Soft Comput..

[20]  Meng Jin,et al.  An improved quantum particle swarm optimization algorithm for environmental economic dispatch , 2020, Expert Syst. Appl..

[21]  S. Deb,et al.  Elephant Herding Optimization , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).

[22]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[23]  Mohammad Ali Abido,et al.  Multiobjective evolutionary algorithms for electric power dispatch problem , 2006, IEEE Transactions on Evolutionary Computation.

[24]  Waree Kongprawechnon,et al.  Ant colony optimisation for economic dispatch problem with non-smooth cost functions , 2010 .

[25]  Mousumi Basu,et al.  Economic environmental dispatch using multi-objective differential evolution , 2011, Appl. Soft Comput..

[26]  Suash Deb,et al.  Monarch butterfly optimization: A comprehensive review , 2021, Expert Syst. Appl..

[27]  Amir H. Gandomi,et al.  The Arithmetic Optimization Algorithm , 2021, Computer Methods in Applied Mechanics and Engineering.

[28]  Cheng-Chien Kuo,et al.  A Novel Coding Scheme for Practical Economic Dispatch by Modified Particle Swarm Approach , 2008, IEEE Transactions on Power Systems.

[29]  Jaspreet Singh Dhillon,et al.  Multiobjective thermal power dispatch using opposition-based greedy heuristic search , 2016 .

[30]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[31]  Chandan Kumar Chanda,et al.  Solution of Economic Load Dispatch Problem Using Biogeography Based Optimization Technique Considering Valve Point Loading Effect , 2017 .

[32]  Ugur Güvenc,et al.  Combined economic and emission dispatch solution using gravitational search algorithm , 2012, Sci. Iran..

[33]  Xiaoqing Bai,et al.  Semi-definite programming-based method for security-constrained unit commitment with operational and optimal power flow constraints , 2009 .

[34]  Amir Hossein Gandomi,et al.  A new improved krill herd algorithm for global numerical optimization , 2014, Neurocomputing.

[35]  K. S. Swarup,et al.  Multi Objective Harmony Search Algorithm For Optimal Power Flow , 2010 .

[36]  Nenad Jovanovic,et al.  Moth Swarm Algorithm for Solving Combined Economic and Emission Dispatch Problem , 2017 .

[37]  A. F. Naiem,et al.  Economic Load Dispatch Problem using Particle Swarm Optimization Technique Considering Wind Power Penetration , 2019, 2019 International Conference on Smart Energy Systems and Technologies (SEST).

[38]  Siddhartha Bhattacharyya,et al.  Border Collie Optimization , 2020, IEEE Access.

[39]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[40]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[41]  Binod Shaw,et al.  Solution of economic dispatch problems by seeker optimization algorithm , 2012, Expert Syst. Appl..

[42]  Ragab A. El-Sehiemy,et al.  Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm , 2016 .

[43]  Jason Gu,et al.  Solution of an Economic Dispatch Problem Through Particle Swarm Optimization: A Detailed Survey – Part II , 2017, IEEE Access.

[44]  Wr Graham,et al.  The potential of future aircraft technology for noise and pollutant emissions reduction , 2014 .

[45]  F. Howard.  THE LIFE HISTORY OF PHYSARUM POLYCEPHALUM , 1931 .

[46]  Abdullah K. Alqallaf,et al.  Solving non-convex economic load dispatch problem via artificial cooperative search algorithm , 2019, Expert Syst. Appl..

[47]  Provas Kumar Roy,et al.  Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem , 2013 .

[48]  Xiaohua Xia,et al.  Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs , 2015 .

[49]  Ji Xiang,et al.  A hybrid non-linear time-varying double-weighted particle swarm optimization for solving non-convex combined environmental economic dispatch problem , 2020, Appl. Soft Comput..

[50]  Juan Li,et al.  Elephant Herding Optimization: Variants, Hybrids, and Applications , 2020, Mathematics.