Single Objective GA Research Articles

Multiobjective Optimization of Microwave Phased Array Excitation for Targeted Tissue Heating With Reduced Channel Power in Hyperthermia Treatment Planning

The multiobjective genetic algorithm (MOGA) is proposed for determining phased array (PA) excitation to selectively heat the tumor with minimal healthy tissue hotspot. Power absorption indicators, such as mean specific absorption rate (SAR) in tumor target (SAR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">targ</sub> ), hotspot to tumor quotient (HTQ), average power absorption ratio (aPA ratio), and 50% iso-SAR tumor coverage (TC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> ), were combined in pairs as an objective function in MOGA. Coupled electromagnetic (EM) and thermal simulations of the heterogeneous breast with locally advanced breast cancer (LABC) were used for evaluating MOGA and single-objective GA (SOGA)-based treatment plans; 25 LABC patient models with varying breast (144.66–1339.3 cc) and tumor (10.53–88.88 cc) volumes and tumor location were employed. The treatment plans were assessed using aforementioned SAR indicators, steady-state thermal metrics in tumors, healthy tissue hotspots in the hyperthermia range (40 °C–44 °C), and channel power consumption of the PA. Among the treatment plans, MOGA with [1/SAR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">targ</sub> , HTQ] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">obj</sub> yielded 104.63% rise in SAR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">targ</sub> across patients with thermal metrics comparable to SOGA with HTQ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">obj</sub> [state of the art (SoA)]. MOGA with [1/SAR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">targ</sub> , HTQ] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">obj</sub> required only 50.5% channel power compared to the SoA, as the tumor was efficiently heated without wasting power in the healthy tissues. This is a significant contribution to developing an affordable PA system.

Multiobjective design optimization and analysis of magnetic flux distribution for slotless permanent magnet brushless DC motor using evolutionary algorithms

Abstract In this paper, Multi-Objective Optimization (MOO) techniques namely: Weighted Sum Method (WSM), Multi Objective Genetic Algorithm (MOGA) and Niched Pareto Genetic Algorithm (NPGA) are proposed for the design optimization and analysis of magnetic flux distribution for a slotless permanent magnet Brushless DC (BLDC) motor. The sensitivity analysis is carried out to identify the design variables of BLDC motor which influence the objective function. The objective functions are conflicting in nature (maximization of output torque and minimization of total volume and losses), hence, are optimized simultaneously by means of MOO algorithms. The proposed MOOs account for four kinds of design variables namely: rotor radius, stator/ rotor axial length, magnet thickness and winding thickness simultaneously to maximize the output torque and to reduce the total volume and total power loss. The conventional MOO such as WSM gives single pareto optimal solution. However, the proposed MOGA and NPGA algorithms create accurate and well-distributed pareto front set with few function evaluations. The performances of the three MOOs are evaluated and compared using the four performance metrics namely: Hyper Volume (HV), Generational Distance (GD), Inverted Generational Distance (IGD) and Spread. From the comparison, it is observed that NPGA gives better results. Using the design parameter obtained from NPGA, the magnetic flux distribution analysis of the BLDC motor is carried out to analyze variation of flux distribution in the different parts of the motor. The results thus obtained are compared with those obtained through Partial differential equation and single objective GA optimization. The detailed thermal analysis is also carried out to analyze the thermal behavior at different parts of the machine for different working conditions in the continuous operation mode and hence, the results obtained are compared with single objective GA optimization. The advantages of MOOs in the design optimization of slotless permanent magnet BLDC motor are highlighted.

Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions.

In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this article, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation problems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their performance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.

Humanoid robot arm performance optimization using multi objective evolutionary algorithm

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria; minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.

Theoretical Considerations of Joint Optimal Model for Water Allocation and Pipe Placement

Water shortage emerges and restricts the urban construction and the social-development due to the rapid expansion of the cities. Recent research mainly focuses on the water resources management or water distribution network management separately. In this paper, a joint optimal model for water allocation and water distribution network management is developed. In the first phase, a multi-objectives linear programming is used for the optimal allocation on multi-water resources for multi-users, in which cost and water conservation are regards as objectives. In the second phase, a non-linear model is proposed, which is solved by single-objective GA linking to EPANET hydraulic solver. The result of the first phase is a parameter of the objective in the second phase.

Optimal design and development of a 2-DOF PKM-based machine tool

This paper presents multiobjective optimization of a typical 2-degree-of-freedom (DOF) parallel kinematic machine (PKM) tool that has only single DOF joints. Nondimensional indices, namely global stiffness index (GSI), global conditioning index (GCI), and workspace index, are considered as the objectives for optimization. The indices GSI and GCI depict the variation of stiffness and dexterity of PKM within the workspace. The leg length and distance between two rails on which actuators slide are treated as design variables as these greatly influence the characteristics of PKM. A multiobjective genetic algorithm (MOGA) approach is implemented in MATLAB to find an efficient solution to this complex optimization problem. Fitness function includes inverse kinematics equations, Jacobian and stiffness matrices to compute and optimize the nondimensional indices. First, the optimal value of each index is obtained by single-objective GA. To further improve the results, a hybrid function PATTERNSEARCH is used. This helps to select appropriate boundary conditions for MOGA. To obtain the optimal values of all the three indices, a multiobjective GA is carried out. The results are compared with a conventional exhaustive search method of optimization. The obtained results show that the use of MOGA enhances the quality of the optimization outcome. Secondly, a prototype has been designed and developed with the optimal dimensions. The actual workspace of the PKM and influence of leg collision on the workspace are studied. Finally, a preliminary experimentation was carried out. A comparison between PKM and the three-axis serial kinematic machine tool is presented.

Tuning of a Proportional-Integral-Derivative Controller using Multi-Objective Non Dominated Sorting Particle Swarm Optimization Applied to pH Control in Continuous Stirred Tank Reactor

Problem statement: Most of the control engineering problems are characterized by several, contradicting, conflicting objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion and using Pareto-optimal solutions. Approach: Non-Dominated Sorting Particle Swarm Optimization algorithm (NSPSO) based approach is used in the design of multiobjective PID controller to find the constant proportional-integral-derivative gains for a chemical neutralization plant. The plant considered in this study is highly non-linear and with varying time delay, provides a challenging test bed for nonlinear control problems. Results: Experimental results confirm that a multi-objective, Paretobased GA search gives a better performance than a single objective GA. Conclusion: Finally, the results for single objective and multiobjective optimization using NSPSO for the neutralization plant are compared. Gain scheduled PID controllers are designed from Pareto front obtained with NSPSO which exhibit good disturbance rejection capability.

Solving the Class Responsibility Assignment Problem in Object-Oriented Analysis with Multi-Objective Genetic Algorithms

In the context of object-oriented analysis and design (OOAD), class responsibility assignment is not an easy skill to acquire. Though there are many methodologies for assigning responsibilities to classes, they all rely on human judgment and decision making. Our objective is to provide decision-making support to reassign methods and attributes to classes in a class diagram. Our solution is based on a multi-objective genetic algorithm (MOGA) and uses class coupling and cohesion measurement for defining fitness functions. Our MOGA takes as input a class diagram to be optimized and suggests possible improvements to it. The choice of a MOGA stems from the fact that there are typically many evaluation criteria that cannot be easily combined into one objective, and several alternative solutions are acceptable for a given OO domain model. Using a carefully selected case study, this paper investigates the application of our proposed MOGA to the class responsibility assignment problem, in the context of object-oriented analysis and domain class models. Our results suggest that the MOGA can help correct suboptimal class responsibility assignment decisions and perform far better than simpler alternative heuristics such as hill climbing and a single-objective GA.

TUNING OF A PROPORTIONAL–INTEGRAL–DERIVATIVE CONTROLLER USING A MULTIOBJECTIVE GENETIC ALGORITHM NONDOMINATED SORTING GENETIC ALGORITHM‐II APPLIED TO A pH PROCESS

ABSTRACT Most control engineering problems are characterized by several, often contradicting, objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion, and using Pareto‐optimal solutions. Here we propose the nondominated sorting genetic algorithm (NSGA‐II) to find the constant proportional–integral–derivative (PID) control gains for a chemical neutralization plant. Known to be highly nonlinear and with varying time delay, this plant provides a challenging test bed for nonlinear control strategies. The results confirm that a multiobjective, Pareto‐based GA search gives a better performance than a single objective GA. The former method was also used to design a gain‐scheduled PID controller. PRACTICAL APPLICATIONSThe control of the pH neutralization process plays a very important role in chemical industries, such as wastewater treatments, polymerization reactions, fatty acid production, biochemical processes, pharmaceuticals, biotechnology and so on. The pH neutralization process shows a strong nonlinear behavior and time‐varying nonlinear characteristics resulted from the variation of the feed components or total ion concentrations. In the case of fermentation by the microorganisms, the growth of microorganisms has a pH optimum similar to most biochemical processes. A pH value drifting away from this optimum often inhibits growth of the essential microorganisms, alters the bacterial population and inhibits the desirable enzymatic activities. The result is a delay in the fermentation process, which in turn results in financial loss.

A multi-objective genetic algorithm approach for adaptive PSS and TCSC-based controller design

This paper presents a multi-objective evolutionary algorithm approach for adaptive design of a power system stabiliser (PSS) and a thyristor controlled series compensator (TCSC)-based controller. The design objective is minimisation of both angle and voltage time trajectory deviations with respect to a post-contingency equilibrium point for a power system installed with a PSS and a TCSC controller. The optimal controller parameters of proposed controllers are coordinately designed. To show the adaptive capabilities of proposed approach, simulation results are presented at various loading conditions with parameter variations. Also, the results are compared with single objective GA approach to show the superiority of the proposed approach.

Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network's probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a basic structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favors sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA.

Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems

We propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and parallel IGA, and evaluate them with both simulated and real users. Combining human evaluation with an optimization system for engineering design enables us to embed domainspecific knowledge that is frequently hard to describe, i.e. subjective criteria, and design preferences. We introduce a new IGA technique to extend the previously introduced sequential single objective GA and multi-objective GA, viz. parallel IGA. Experimental evaluation of three algorithms with a multi-objective manufacturing plant layout design task shows that the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and that the multi-objective IGA gives the most diverse results and fastest convergence to a stable set of qualitatively optimum solutions, although the parallel IGA provides the best quantitative fitness convergence.