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.

Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints

2019 ◽  
Vol 31 (4) ◽  
pp. 689-702 ◽  
Author(s):  
Juliane Müller ◽  
Marcus Day
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Black Box ◽  
Direct Search ◽  
Test Problems ◽  
Large Set ◽  
Mesh Adaptive Direct Search ◽  
Surrogate Optimization ◽  
Computationally Expensive ◽  
Hidden Constraints

We introduce the algorithm SHEBO (surrogate optimization of problems with hidden constraints and expensive black-box objectives), an efficient optimization algorithm that employs surrogate models to solve computationally expensive black-box simulation optimization problems that have hidden constraints. Hidden constraints are encountered when the objective function evaluation does not return a value for a parameter vector. These constraints are often encountered in optimization problems in which the objective function is computed by a black-box simulation code. SHEBO uses a combination of local and global search strategies together with an evaluability prediction function and a dynamically adjusted evaluability threshold to iteratively select new sample points. We compare the performance of our algorithm with that of the mesh-based algorithms mesh adaptive direct search (MADS, NOMAD [nonlinear optimization by mesh adaptive direct search] implementation) and implicit filtering and SNOBFIT (stable noisy optimization by branch and fit), which assigns artificial function values to points that violate the hidden constraints. Our numerical experiments for a large set of test problems with 2–30 dimensions and a 31-dimensional real-world application problem arising in combustion simulation show that SHEBO is an efficient solver that outperforms the other methods for many test problems.

Plant propagation algorithm for integration of distributed generation in power systems

2018 ◽  
Vol 37 (1) ◽  
pp. 401-417
Author(s):  
Shreya Mahajan ◽  
Shelly Vadhera
Keyword(s):  
Power System ◽  
Objective Function ◽  
Distributed Generation ◽  
Weighting Factor ◽  
Voltage Profile ◽  
Plant Propagation ◽  
Content Type ◽  
Multi Objective ◽  
Propagation Algorithm ◽  
Optimisation Techniques

Purpose The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance. Design/methodology/approach Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system. Findings The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes. Originality/value This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit.

INTERACTIVE ROBUST CONE CONTRACTION METHOD FOR MULTIPLE OBJECTIVE OPTIMIZATION PROBLEMS

2012 ◽  
Vol 11 (02) ◽  
pp. 327-357 ◽  
Author(s):  
MIŁOSZ KADZIŃSKI ◽  
ROMAN SŁOWIŃSKI
Keyword(s):  
Optimization Problems ◽  
Pairwise Comparison ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Nondominated Solutions ◽  
Nondominated Points ◽  
Current Sample ◽  
Evaluation Space ◽  
Problem Setting ◽  
A Current

We introduce a new interactive procedure for multiple objective optimization problems. The identification of the most preferred solution is achieved by means of a systematic dialogue with the decision maker (DM) during which (s)he specifies pairwise comparisons of nondominated solutions from a current sample. We represent this preference information by a compatible form of the achievement scalarizing function, i.e., we are searching for weights of objectives which ensure that the reference solutions are compared by the function in the same way as by the DM. Directions of the isoquants of all compatible achievement scalarizing functions create a cone in the evaluation space, with the origin in a reference point. In successive iterations, each new pairwise comparison of solutions contracts the cone which is zooming on a subregion of nondominated points of greatest interest for the DM. The procedure ends when at least one satisfactory solution is selected or when the DM comes to conclusion that there is no such solution for the current problem setting.

A Discrete Data Base Multiple Objective Optimization of Milling Operation Through Geometric Programming

1990 ◽  
Vol 112 (4) ◽  
pp. 368-374 ◽  
Author(s):  
N. K. Jha
Keyword(s):  
Objective Function ◽  
Production Planning ◽  
Process Planning ◽  
Geometric Programming ◽  
Multiple Objective ◽  
Optimum Process ◽  
Multiple Objective Optimization ◽  
Objective Functions ◽  
Milling Operations ◽  
Milling Operation

The optimum process planning of the milling operation has been attempted through multiple objectives. A multiple objective function based on cost of production and rate of production in milling operations has been developed. The unified objective function thus developed serves as true arbiter balancing the values and objectives of local or individual objective functions. Most of the machine settings are discrete in nature and this has been considered in computerized production planning of milling. The complete approach has been demonstrated through an example.

AN IMPROVED POPULATION BASED OPTIMIZATION SOLUTION METHOD BY COMBINING LOCAL AND GLOBAL SEARCHING

2007 ◽  
Vol 16 (05) ◽  
pp. 907-915
Author(s):  
WEI JIANG ◽  
XIAO-LONG WANG ◽  
XIU-LI PANG
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Problems ◽  
Population Based ◽  
Search Algorithms ◽  
Global Search ◽  
Local Optimum ◽  
Hard Problems ◽  
The Common ◽  
Optimum Solution

Optimization Solution Task is a typical and important task in many applications. Many optimization problems have been proved to be NP-hard problems, which cannot be solved by some predefined mathematic formulae. In this case, computer aided method is very helpful. While some local search algorithms are easily to fall into a local optimum solution. On contrast, the population based methods, such as Genetic Algorithms, Artificial Immune System, Autonomy Oriented Computing, are global search algorithms. However, they are not good at the local search. In this paper, an improved method is proposed by combining the local and global search ability, so as to improve the performance in terms of the convergence speed and the convergence reliability. We construct a generic form to deal with the common objective function space or the objective function with the partial derivative. In addition, we present an n-hold method in population based evolution method. The experiments indicate that our approach can effectively improve the convergence reliability, which is much concerned in some applications with the expensive executing expense.

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