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.

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 Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

2011 ◽  
Vol 225 (11) ◽  
pp. 1211-1227 ◽  
Author(s):  
M Vasile ◽  
F Zuiani
Keyword(s):  
Selection Criterion ◽  
Search Space ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Agent Based ◽  
Multi Objective ◽  
Collaborative Search ◽  
Multi Agent ◽  
Space Trajectory Design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.

scholarly journals Multi-Objective Optimal Operations Based on Improved NSGA-II for Hanjiang to Wei River Water Diversion Project, China

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1159 ◽  
Author(s):  
Lianzhou Wu ◽  
Tao Bai ◽  
Qiang Huang ◽  
Jian Wei ◽  
Xia Liu
Keyword(s):  
Power Generation ◽  
Search Space ◽  
Water Transfer ◽  
Water Diversion ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Water Diversion Project ◽  
Basin Water ◽  
Wei River

It is important to investigate the laws of reservoir multi-objective optimization operations, because it can obtain the best benefits from inter-basin water transfer projects to mitigate water shortage in intake areas. Given the multifaceted demands of the Hanjiang to Wei River Water Diversion Project, China (referred hereafter as “the Project”), an easy-to-operate multi-objective optimal model based on simulation is built and applied to search the multi-objective optimization operation rules between power generation and energy consumption. The Project includes two reservoirs connected by a water transfer tunnel. One is Huangjinxia, located in the mainstream of Hanjiang with abundant inflow but no regulation ability, and the other is Sanhekou, located in the tributary of Hanjiang with multi-year regulation ability but less water. The layout of the Project increases the difficulty of reservoir joint optimization operations. Therefore, an improved Non-dominated Sorting Genetic Algorithm-II (I-NSGA-II) with a feasible search space is proposed to solve the model based on long-term series data. The results show that: (1) The validated simulation model is helpful to obtain Pareto front curves to reveal the rules between power generation and energy consumption. (2) Choosing a reasonable search step size to build a feasible search space based on simulation results for the I-NSGA-II can help find more optimized solutions. Considering the influence of the initial populations of the algorithm and limited computing ability of computers, the qualified rate of Pareto points solved by I-NSGA-II are superior to NSGA-II. (3) According to the characteristics of the Project, water transfer ratio threshold value of two reservoirs are quantified for maximize economic benefits. Moreover, the flood season is a critical operation period for the Project, in which both reservoirs should supply more water to intake areas to ensure the energy balanced of the entire system. The findings provide an easy-to-operate multi-objective operation model with the I-NSGA-II that can easily be applied in optimal management of inter-basin water transfer projects by relevant authorities.

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.

Multi-Objective Optimization With Multiple Spatially Distributed Surrogates

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Kalyan Shankar Bhattacharjee ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
A Priori ◽  
Search Space ◽  
Nsga Ii ◽  
Multi Objective ◽  
Engineering Design Optimization ◽  
Spatially Distributed ◽  
Computationally Expensive

In engineering design optimization, evaluation of a single solution (design) often requires running one or more computationally expensive simulations. Surrogate assisted optimization (SAO) approaches have long been used for solving such problems, in which approximations/surrogates are used in lieu of computationally expensive simulations during the course of search. Existing SAO approaches often use the same type of approximation model to represent all objectives and constraints in all regions of the search space. The selection of a type of surrogate model over another is nontrivial and an a priori choice limits flexibility in representation. In this paper, we introduce a multi-objective evolutionary algorithm (EA) with multiple adaptive spatially distributed surrogates. Instead of a single global surrogate, local surrogates of multiple types are constructed in the neighborhood of each offspring solution and a multi-objective search is conducted using the best surrogate for each objective and constraint function. The proposed approach offers flexibility of representation by capitalizing on the benefits offered by various types of surrogates in different regions of the search space. The approach is also immune to illvalidation since approximated and truly evaluated solutions are not ranked together. The performance of the proposed surrogate assisted multi-objective algorithm (SAMO) is compared with baseline nondominated sorting genetic algorithm II (NSGA-II) and NSGA-II embedded with global and local surrogates of various types. The performance of the proposed approach is quantitatively assessed using several engineering design optimization problems. The numerical experiments demonstrate competence and consistency of SAMO.

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.

Optimisation of Assembly Line Balancing Type-E with Resource Constraints Using NSGA-II

2016 ◽  
Vol 701 ◽  
pp. 195-199 ◽  
Author(s):  
Masitah Jusop ◽  
Mohd Fadzil Faisae Ab Rashid
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Resource Constraints ◽  
Hybrid Genetic Algorithm ◽  
Assembly Line Balancing ◽  
Search Space ◽  
Line Balancing ◽  
Benchmark Problems ◽  
Nsga Ii ◽  
Multi Objective Genetic Algorithm

Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that any assembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.

scholarly journals MULTI-OBJECTIVE CALIBRATION OF IBIS MODEL BY GENETIC ALGORITHM WITH PARAMETRIC SENSITIVITY ANALYSIS

2016 ◽  
Vol 38 ◽  
pp. 90
Author(s):  
Amarísio Da Silva Araújo ◽  
Haroldo De Campos Velho ◽  
Lu Minjiao
Keyword(s):  
Genetic Algorithm ◽  
Good Description ◽  
Synthetic Data ◽  
Search Space ◽  
Adjustment Process ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Integrated Biosphere Simulator ◽  
Ibis Model

Atmospheric circulation models combine different modules for a good description of the atmospheric dynamics. One of these modules is the representation of surface coverage, since the dynamics depends on the interaction between the atmosphere and the surface of the planet. However, these modules depend on a number of parameters that need to be adjusted. The parameter adjustment process is called model calibration. In this study, the IBIS (Integrated Biosphere Simulator) model is calibrated following a multi-objective strategy. The Pareto set, which embraces the non-dominated solutions in the search space of objective functions, is determined by a version of multi-objective genetic algorithm (NSGA-II). The model sensitivity to the parameters is evaluated by the Morris’ method. Synthetic data for calibration were obtained from the Tapajós National Forest (FloNa Tapajós), located near to the 67 km from Santarém-Cuiabá highway (2,51S, 54,58W).

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

2020 ◽  
pp. 1573-1593
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul ◽  
Heyam H. Al-Baity
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Space ◽  
Constraint Handling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Handling Mechanism ◽  
Pareto Fronts

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

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.

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