scholarly journals Hierarchical Representation Based Constrained Multi-objective Evolutionary Optimisation of Molecular Structures

2018 ◽  
Vol 63 (1) ◽  
pp. 210-225 ◽  
Author(s):  
Gyula Dörgő ◽  
János Abonyi
Keyword(s):  
Molecular Design ◽  
Molecular Structures ◽  
Benchmark Problems ◽  
Structural Constraints ◽  
Genetic Operators ◽  
Nsga Ii ◽  
Design Problems ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Evolutionary Optimisation

We propose an efficient algorithm to generate Pareto optimal set of reliable molecular structures represented by group contribution methods. To effectively handle structural constraints we introduce goal oriented genetic operators to the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The constraints are defined based on the hierarchical categorisation of the molecular fragments. The efficiency of the approach is tested on several benchmark problems. The proposed approach is highly efficient to solve the molecular design problems, as proven by the presented benchmark and refrigerant design problems.

scholarly journals Group Contribution Method-based Multi-objective Evolutionary Molecular Design

2016 ◽  
Vol 44 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Gyula Dörgő ◽  
János Abonyi
Keyword(s):  
Molecular Design ◽  
Estimation Method ◽  
Chemical Properties ◽  
Search Space ◽  
Molecular Structures ◽  
Benchmark Problems ◽  
Structural Constraints ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pharmaceutical Industries

Abstract The search for compounds exhibiting desired physical and chemical properties is an essential, yet complex problem in the chemical, petrochemical, and pharmaceutical industries. During the formulation of this optimization-based design problem two tasks must be taken into consideration: the automated generation of feasible molecular structures and the estimation of macroscopic properties based on the resultant structures. For this structural characteristic-based property prediction task numerous methods are available. However, the inverse problem, the design of a chemical compound exhibiting a set of desired properties from a given set of fragments is not so well studied. Since in general design problems molecular structures exhibiting several and sometimes conflicting properties should be optimized, we proposed a methodology based on the modification of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The originally huge chemical search space is conveniently described by the Joback estimation method. The efficiency of the algorithm was enhanced by soft and hard structural constraints, which expedite the search for feasible molecules. These constraints are related to the number of available groups (fragments), the octet rule and the validity of the branches in the molecule. These constraints are also used to introduce a special genetic operator that improves the individuals of the populations to ensure the estimation of the properties is based on only reliable structures. The applicability of the proposed method is tested on several benchmark problems.

Multi-objective optimisation of cost–benefit of urban flood management using a 1D2D coupled model

2011 ◽  
Vol 63 (5) ◽  
pp. 1053-1059 ◽  
Author(s):  
S. W. Delelegn ◽  
A. Pathirana ◽  
B. Gersonius ◽  
A. G. Adeogun ◽  
K. Vairavamoorthy
Keyword(s):  
Flow Model ◽  
Coupled Model ◽  
Cost Benefit ◽  
Flood Management ◽  
Flood Damage ◽  
Surface Flow ◽  
Nsga Ii ◽  
Urban Flood ◽  
Multi Objective ◽  
Evolutionary Optimisation

This paper presents a multi-objective optimisation (MOO) tool for urban drainage management that is based on a 1D2D coupled model of SWMM5 (1D sub-surface flow model) and BreZo (2D surface flow model). This coupled model is linked with NSGA-II, which is an Evolutionary Algorithm-based optimiser. Previously the combination of a surface/sub-surface flow model and evolutionary optimisation has been considered to be infeasible due to the computational demands. The 1D2D coupled model used here shows a computational efficiency that is acceptable for optimisation. This technological advance is the result of the application of a triangular irregular discretisation process and an explicit finite volume solver in the 2D surface flow model. Besides that, OpenMP based parallelisation was employed at optimiser level to further improve the computational speed of the MOO tool. The MOO tool has been applied to an existing sewer network in West Garforth, UK. This application demonstrates the advantages of using multi-objective optimisation by providing an easy-to-comprehend Pareto-optimal front (relating investment cost to expected flood damage) that could be used for decision making processes, without repeatedly going through the modelling–optimisation stage.

On Benchmark Problems and Metrics for Decision Space Performance Analysis in Multi-Objective Optimization

2017 ◽  
Vol 16 (01) ◽  
pp. 1750006 ◽  
Author(s):  
Bin Zhang ◽  
Kamran Shafi ◽  
Hussein Abbass
Keyword(s):  
Performance Analysis ◽  
Fitness Landscape ◽  
Benchmark Problems ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Nsga Ii ◽  
Multi Objective ◽  
Objective Space ◽  
Problem Instances

A number of benchmark problems exist for evaluating multi-objective evolutionary algorithms (MOEAs) in the objective space. However, the decision space performance analysis is a recent and relatively less explored topic in evolutionary multi-objective optimization research. Among other implications, such analysis can lead to designing more realistic test problems, gaining better understanding about optimal and robust design areas, and design and evaluation of knowledge-based optimization algorithms. This paper complements the existing research in this area and proposes a new method to generate multi-objective optimization test problems with clustered Pareto sets in hyper-rectangular defined areas of decision space. The test problem is parametrized to control number of decision variables, number and position of optimal areas in the decision space and modality of fitness landscape. Three leading MOEAs, including NSGA-II, NSGA-III, and MOEA/D, are evaluated on a number of problem instances with varying characteristics. A new metric is proposed that measures the performance of algorithms in terms of their coverage of the optimal areas in the decision space. The empirical analysis presented in this research shows that the decision space performance may not necessarily be reflective of the objective space performance and that all algorithms are sensitive to population size parameter for the new test problems.

scholarly journals Sequential Scheduling Method for FJSP with Multi-Objective under Mixed Work Calendars

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 888
Author(s):  
Qiang Zeng ◽  
Menghua Wang ◽  
Ling Shen ◽  
Hongna Song
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Genetic Operators ◽  
Nsga Ii ◽  
Multi Objective ◽  
Elite Strategy ◽  
Scheduling Method ◽  
Encoding Method

A sequential scheduling method for multi-objective, flexible job-shop scheduling problem (FJSP) work calendars is proposed. Firstly, the sequential scheduling problem for the multi-objective FJSP under mixed work calendars was described. Secondly, two key technologies to solve such a problem were proposed: one was a time-reckoning technology based on the machine’s work calendar, the other was a sequential scheduling technology. Then, a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) was designed to solve the problem. In the algorithm, a two-segment encoding method was used to encode the chromosome. A two-segment crossover and mutation operator were used with an improved strategy of genetic operators therein to ensure feasibility of the chromosomes. Time-reckoning technology was used to calculate start and end time of each process. The sequential scheduling technology was used to implement sequential scheduling. The case study shows that the proposed method can obtain an effective Pareto set of the sequential scheduling problem for multi-objective FJSP under mixed work calendars within an acceptable time.

EFFICIENCY INCREASE OF MULTI-OBJECTIVE GENETIC ALGORITHMS USING THE CLUSTERIZATION OF A POPULATION IN THE VARIABLE SPACE

2019 ◽  
pp. 13-20
Author(s):  
P. V. Kazakov
Keyword(s):  
Genetic Algorithm ◽  
Benchmark Problems ◽  
Additional Parameter ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Fitness Value ◽  
Efficiency Increase ◽  
Variable Space ◽  
Special Rule

The paper introduces a new manner for improving of obtained by MOGA (Multi-Objective Genetic Algorithm) solutions. It is based on the concept of dividing the population into set of clusters according to solutions similarity. In different of most MOGA the clusterization of population is implemented in the variable space, enables to enhance diversity of population and to increase the number of non-dominated solutions. The special procedures for the clustering of current population and copying the clusters in the next population were developed. The dominance principal by fitness-value is used for clustering. The number of clusters depends on additional parameter the radius of cluster’s hypersphere that is determined experimentally. By the special rule the individuals corresponded to centroids of clusters are copied in the new population. The clusters are recalculated for every population. The influence of the radius cluster to the number of non-dominated solutions variation was studied. The cluster modification should be integrated into any multi-objective genetic algorithm. By the analytical evaluation has been studied, this MOGA modification has additional computationally complexity from linear to quadratic. In experiments it was tested with the evolutionary algorithms SPEA2, NSGA-II on the special benchmark problems (DTLZ) with a various number of criteria using the set of performance indices. The used clustering in the variable space algorithms were achieved a better distribution and convergence to the true Paretofront in some cases.

Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

2018 ◽  
Vol 11 (1) ◽  
pp. 20-46 ◽  
Author(s):  
K. Shankar ◽  
Akshay S. Baviskar
Keyword(s):  
Engineering Design ◽  
Nsga Ii ◽  
Design Problems ◽  
Content Type ◽  
Multi Objective ◽  
Decision Variables ◽  
Engineering Design Problems ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Utopia Point

Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

A Novel Multi-Objective Competitive Swarm Optimization Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 114-129
Author(s):  
Prabhujit Mohapatra ◽  
Kedar Nath Das ◽  
Santanu Roy ◽  
Ram Kumar ◽  
Nilanjan Dey
Keyword(s):  
Optimization Algorithm ◽  
State Of The Art ◽  
Statistical Tests ◽  
The Other ◽  
Benchmark Problems ◽  
Dominance Relationship ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Multi Objective ◽  
External Archive

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

scholarly journals Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems

2015 ◽  
Vol 63 (3) ◽  
pp. 791-798 ◽  
Author(s):  
M.B. Gorzałczany ◽  
F. Rudziński
Keyword(s):  
Fuzzy Rule ◽  
Optimization Methods ◽  
Evolutionary Optimization ◽  
Rule Base ◽  
Genetic Operators ◽  
Nsga Ii ◽  
Open Problems ◽  
Trade Off ◽  
Fuzzy Partition ◽  
Multi Objective

Abstract The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonstrate that our approach is an effective tool for handling FRBSs’ accuracy-interpretability trade-off, i.e, designing FRBSs characterized by various levels of such a trade-off (in particular, for designing highly interpretability-oriented systems of still competitive accuracy).

scholarly journals A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1625
Author(s):  
Andrea Ponti ◽  
Antonio Candelieri ◽  
Francesco Archetti
Keyword(s):  
Water Distribution ◽  
Sensor Placement ◽  
Distribution Networks ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Genetic Operators ◽  
Placement Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Dynamic Representation

The sensor placement problem is modeled as a multi-objective optimization problem with Boolean decision variables. A new multi objective evolutionary algorithm (MOEA) is proposed for approximating and analyzing the set of Pareto optimal solutions. The evaluation of the objective functions requires the execution of a hydraulic simulation model of the network. To organize the simulation results a data structure is proposed which enables the dynamic representation of a sensor placement and its fitness as a heatmap. This allows the definition of information spaces, in which the fitness of a placement can be represented as a matrix or, in probabilistic terms as a histogram. The key element in the new algorithm is this probabilistic representation which is embedded in a space endowed with a metric based on a specific notion of distance. Among several distances between probability distributions the Wasserstein (WST) distance has been selected: WST has enabled to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm has been tested on a benchmark water distribution network with two objective functions showing an improvement over NSGA-II, in particular for low generation counts, making it a good candidate for expensive black-box multi-objective optimization

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