scholarly journals Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem

2017 ◽  
Author(s):  
Sayan Nag
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Economic Load Dispatch ◽  
Objective Functions ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Highly Nonlinear ◽  
Plant Intelligence ◽  
Classical Optimization

Optimization problems in design engineering are complex by nature, often because of the involvement of critical objective functions accompanied by a number of rigid constraints associated with the products involved. One such problem is Economic Load Dispatch (ED) problem which focuses on the optimization of the fuel cost while satisfying some system constraints. Classical optimization algorithms are not sufficient and also inefficient for the ED problem involving highly nonlinear, and non-convex functions both in the objective and in the constraints. This led to the development of metaheuristic optimization approaches which can solve the ED problem almost efficiently. This paper presents a novel robust plant intelligence based Adaptive Plant Propagation Algorithm (APPA) which is used to solve the classical ED problem. The application of the proposed method to the 3-generator and 6-generator systems shows the efficiency and robustness of the proposed algorithm. A comparative study with another state-of-the-art algorithm (APSO) demonstrates the quality of the solution achieved by the proposed method along with the convergence characteristics of the proposed approach.

scholarly journals A Plant Propagation Algorithm for Constrained Engineering Optimisation Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sulaiman ◽  
Abdellah Salhi ◽  
Birsen Irem Selamoglu ◽  
Omar Bahaaldin Kirikchi
Keyword(s):  
State Of The Art ◽  
The Novel ◽  
High Quality ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Highly Nonlinear ◽  
Continuous Problems ◽  
Comparative Results ◽  
Optimisation Algorithms ◽  
Selection Of

Optimisation problems arising in industry are some of the hardest, often because of the tight specifications of the products involved. They are almost invariably constrained and they involve highly nonlinear, and non-convex functions both in the objective and in the constraints. It is also often the case that the solutions required must be of high quality and obtained in realistic times. Although there are already a number of well performing optimisation algorithms for such problems, here we consider the novel Plant Propagation Algorithm (PPA) which on continuous problems seems to be very competitive. It is presented in a modified form to handle a selection of problems of interest. Comparative results obtained with PPA and state-of-the-art optimisation algorithms of the Nature-inspired type are presented and discussed. On this selection of problems, PPA is found to be as good as and in some cases superior to these algorithms.

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

scholarly journals Fresa: A Plant Propagation Algorithm for Black-Box Single and Multiple Objective Optimization

2021 ◽  
Vol 2 (4) ◽  
Keyword(s):  
Mathematical Programming ◽  
Objective Function ◽  
Optimization Problems ◽  
Population Based ◽  
Black Box ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Multiple Dispatch

Fresa implements a nature inspired plant propagation algorithm for the solution of single and multiple objective optimization problems. The method is population based and evolutionary. Treating the objective function as a black box, the implementation is able to solve problems exhibiting behaviour that is challenging for mathematical programming methods. Fresa is easily adapted to new problems which may benefit from bespoke representations of solutions by taking advantage of the dynamic typing and multiple dispatch capabilities of the Julia language. Further, the support for threads in Julia enables an efficient implementation on multi-core computers.

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

2015 ◽  
Vol 23 (1) ◽  
pp. 69-100 ◽  
Author(s):  
Handing Wang ◽  
Licheng Jiao ◽  
Ronghua Shang ◽  
Shan He ◽  
Fang Liu
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Space ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

scholarly journals Robust Multi-Area Economic Dispatch Using Coulomb’s and Franklin’s Laws Based Optimizer

2020 ◽  
Vol 10 (4) ◽  
pp. 235-251
Author(s):  
Vadugapalayam Ponnuvel Sakthivel ◽  
Murugesan Suman ◽  
Palanigounder Duraisamy Sathya
Keyword(s):  
State Of The Art ◽  
Cost Effective ◽  
New Physics ◽  
Standard Test ◽  
Economic Load Dispatch ◽  
Exchange Market ◽  
Suggested Approach ◽  
Generation Planning ◽  
Excess Power

The multi-area economic load dispatch (MAELD) can reduce running costs through making the areas with more cost-effective units produce more energy.  The excess power is transferred to the areas with expensive units. This paper contributes a new physics inspired metaheuristic approach called the Coulomb’s and Franklin’s laws based optimizer (CFLBO) to solve the MAELD problem. The CFLBO approach is developed from Coulomb’s and Franklin’s theories, which comprise attraction/repulsion, probabilistic ionization, and contact stages. The effectiveness of the envisaged CFLBO approach has been examined on three standard test systems with various areas. Results obtained by the CFLBO approach are compared with the exchange market algorithm (EMA) and the existing state-of-the-art approaches to deal with MAELD. Numerical outcomes show the benefits of the quick convergence and better quality of the suggested approach compared to existing strategies. Consequently, the proposed approach is a helpful tool for generation planning in MAELD problems.

scholarly journals A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

2020 ◽  
Vol 10 (17) ◽  
pp. 5791 ◽  
Author(s):  
Mohammad Dehghani ◽  
Mohammad Mardaneh ◽  
Josep M. Guerrero ◽  
Om Parkash Malik ◽  
Ricardo A. Ramirez-Mendoza ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Standard Test ◽  
Objective Functions ◽  
Test Function ◽  
Commitment Problem ◽  
New Ideas ◽  
Patient Optimization ◽  
Research World

Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed by simulating the process of treating patients by a physician. The treatment process has three phases, including vaccination, drug administration, and surgery. The efficiency of the proposed algorithm in solving optimization problems compared to eight other optimization algorithms on a benchmark standard test function with 23 objective functions is been evaluated. The results obtained from this comparison indicate the superiority and quality of DPO in solving optimization problems in various sciences. The proposed algorithm is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system.

scholarly journals A Brief Look at Multi-Criteria Problems: Multi-Threshold Optimization versus Pareto-Optimization

2020 ◽  
Author(s):  
Nodari Vakhania ◽  
Frank Werner
Keyword(s):  
Optimization Problems ◽  
Optimality Criteria ◽  
General Notion ◽  
Objective Functions ◽  
Objective Criterion ◽  
Solution Quality ◽  
Pareto Optimal Set ◽  
Threshold Optimization ◽  
Feasible Solutions

Multi-objective optimization problems are important as they arise in many practical circumstances. In such problems, there is no general notion of optimality, as there are different objective criteria which can be contradictory. In practice, often there is no unique optimality criterion for measuring the solution quality. The latter is rather determined by the value of the solution for each objective criterion. In fact, a practitioner seeks for a solution that has an acceptable value of each of the objective functions and, in practice, there may be different tolerances to the quality of the delivered solution for different objective functions: for some objective criteria, solutions that are far away from an optimal one can be acceptable. Traditional Pareto-optimality approach aims to create all non-dominated feasible solutions in respect to all the optimality criteria. This often requires an inadmissible time. Besides, it is not evident how to choose an appropriate solution from the Pareto-optimal set of feasible solutions, which can be very large. Here we propose a new approach and call it multi-threshold optimization setting that takes into account different requirements for different objective criteria and so is more flexible and can often be solved in a more efficient way.

scholarly journals Seed based plant propagation algorithm for multiple travelling salesman problem

2018 ◽  
Vol 7 (3.3) ◽  
pp. 515
Author(s):  
S Kalaiarasi ◽  
P Sriramya
Keyword(s):  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Routing Algorithm ◽  
Complex Problem ◽  
Travelling Salesman ◽  
Biologically Inspired ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Biologically Inspired Algorithms ◽  
Multiple Travelling Salesman Problem

Multiple Travelling Salesman Problem is a complex problem in which route for a salesman is assigned to visit a city that has various hurdles such as congested road, damaged road, etc. In recent years biologically inspired algorithms are most widely used to solve many optimization problems. Here seed based plant propagation algorithm is applied for the multiple travelling salesman problem that is also a optimization problem, and the result is compared with a short-cut routing algorithm. The result shows that Seed based Propagation Algorithm is easy to implement since it has few parameters to be utilized and also time complexity is reduced when implemented in multiple travelling salesman problem.  

scholarly journals A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-26 ◽  
Author(s):  
Enrique Castillo ◽  
Zacarías Grande ◽  
Aida Calviño ◽  
W. Y. Szeto ◽  
Hong K. Lo
Keyword(s):  
Relative Error ◽  
Optimization Problems ◽  
State Of The Art ◽  
Mathematical Optimization ◽  
Integrated Approach ◽  
Specific Model ◽  
Traffic Networks ◽  
Objective Functions ◽  
Estimation And Prediction ◽  
Prediction Problems

A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.

Convergence analysis of the plant propagation algorithm for continuous global optimization

2018 ◽  
Vol 52 (2) ◽  
pp. 429-438 ◽  
Author(s):  
Nassim Brahimi ◽  
Abdellah Salhi ◽  
Megdouda Ourbih-Tari
Keyword(s):  
Global Optimization ◽  
Convergence Analysis ◽  
Convergence Theorem ◽  
Optimization Problems ◽  
Stochastic Algorithm ◽  
Strawberry Plant ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Continuous Global Optimization ◽  
General Convergence

The Plant Propagation Algorithm (PPA) is a Nature-Inspired stochastic algorithm, which emulates the way plants, in particular the strawberry plant, propagate using runners. It has been experimentally tested both on unconstrained and constrained continuous global optimization problems and was found to be competitive against well established algorithms. This paper is concerned with its convergence analysis. It first puts forward a general convergence theorem for a large class of random algorithms, before the PPA convergence theorem is derived and proved. It then illustrates the results on simple problems.

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