Review on Real Coded Genetic Algorithms Used in Multiobjective Optimization

Many real-world design problems involve simultaneous optimisation of multiple objectives. The considered grillage design is a multi-objective optimisation problem since there are two objective functions, namely: the volume design and the cost design. Thus, the solution of this optimisation problem requires specialised method suitable for multi-objective problems. In this article, a multi-objective design optimisation using real-coded genetic algorithm is proposed for the Pareto-optimality of grillage system. The objective functions in the optimisation problem measure the design sensitivities in grillage system. The non-linear constrained multi-objective optimisation is very important from the point of view of practical problem solving. Therefore, the real-coded genetic algorithm with multiple genetic operators is proposed to find the optimum grillage system without handling any of the penalty functions. Direct strength calculation defined from the class rules of DNV was applied for structural design of grillage system. The hybrid method (real-coded genetic algorithm including the non-dominated sorting and sharing approaches) performs a marvellous explorability in finding a diverse set of solutions and in converging near the true Pareto-optimal set. The results obtained are very encouraging since they show that we can produce an important portion of the Pareto-front at a very low computational time frame.

Y-stiffened panel multi-objective optimization using genetic algorithm

This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

Surrogate-based method can dramatically reduce the number of expensive function evaluations in real-world multi- objective optimization problems (MOP). When the number of objectives is small, using surrogate models combined with expected hypervolume improvement (EHVI) infill sampling criterion (ISC) has been proved to be efficient to provide a set of solutions with good diversity and good proximity to the Pareto front (PF) in aerodynamic shape optimization. However, traditional hypervolume-based infilling strategies use only one kind of ISC to generate one or multiple sample points, the advantages of various kinds of ISC cannot be comprehensively utilized and the parallelization is not easy to implement. This paper proposes a combined multi-point infilling strategy based on Kriging models and develops an efficient global multi-objective constrained optimization method (EGMOCO) to solve multi- objective aerodynamic shape optimization with complex constraints. Multiple sample points are generated by using four ISC considering hypervolume at each iteration and then evaluated in parallel. Firstly, the performance of EGMOCO is compared with that of single criterion EHVI strategy on six benchmarks within the same computational budget to prove its effectiveness, and then EGMOCO is implemented in an aerodynamic shape optimization problem with complex constraints. The result shows that EGMOCO has good performance in balancing local exploitation and global exploration with faster convergence rate and high robustness, the whole PF can be fully explored in limited evaluations and the constraint handling is effective especially for real-world problems with complex and nonlinear constraints, the comprehensive aerodynamic performance of the airfoil is greatly improved. It can be confirmed that Kriging-based multi-objective optimization method combined with multi-point infilling strategy performs better than single infilling criterion EHVI, since different sample infilling criteria can complement with each other, both local exploitation and global exploration can be considered and well balanced.

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations. To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.

Camera calibration is an essential issue in many computer vision tasks in which quantitative information of a scene is to be derived from its images. It is concerned with the determination of a set of parameters from the given images. In literature, it has been modeled as a nonlinear global optimization problem and has been solved using various optimization techniques. In this article, a recently developed variant of a very popular global optimization technique—the particle swarm optimization (PSO) algorithm—has been used for solving this problem for a stereo camera system modeled by pin-hole camera model. Extensive experiments have been performed on synthetic data to test the applicability of the technique to this problem. The simulation results, which have been compared with those obtained by a real coded genetic algorithm (RCGA) in literature, show that the proposed PSO performs a bit better than RCGA in terms of computational effort.

Improving voltage stability, reducing real power loss (PL) and voltage deviation (VD) are the most important tasks in the operation of electrical power systems. Voltage instability and voltage collapse are the severe problems which may take place because of deficit reactive power at load buses due to increased loading or contingencies. In this paper, the problem of obtaining optimal location and size of SVC is formulated as true multi-objective optimization problem for simultaneous minimization of the two objectives namely real power losses and load bus voltage deviation. The two algorithms real coded genetic algorithm (RCGA) and non-dominated sorting genetic algorithm-II (NSGA-II) with a feature of adoptive crowding distance have been used for solving nonlinear constrained multi-objective optimization problem. Both the algorithms have been used for obtaining optimal location and sizing of SVC. Voltage security of the power system has also been analyzed separately for all placement of SVC to ensure secure operation of the system. The proposed approaches have been implemented on IEEE 30-bus test system. The simulation results of the two algorithms have been compared for solution quality, computational complexity and computational time. It has been found that NSGA-II presents better performance in solving multi-objective optimization problem and also in obtaining a diverse set of solutions which converge near the true Pareto-optimal front. The simulation results of NSGA-II have also been presented to exhibit the capabilities of the algorithm to generate well-distributed Pareto-optimal front.

Optimal reactive power dispatch using self-adaptive real coded genetic algorithm

The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems. History: Accepted by Alice Smith and Ted Ralphs, Area Editors for Software Tools. Funding: This work was supported by US DOE, Office of Science, Advanced Scientific Computing Research, SciDAC Program [Grants DE-AC02-05CH11231 and DE-AC02-06CH11357]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0250 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0250 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Generate optimal grasping trajectories to the end-effector using an improved genetic algorithm

Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, Multi-Objective Particle Swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem.

This paper proposes one novel design method for FIR low pass filter design using a recently proposed heuristic search algorithm called gravitational search algorithm (GSA). Various swarm based algorithms like real coded genetic algorithm (RGA), conventional particle swarm optimization (PSO), differential evolution (DE) and the proposed gravitational search algorithm (GSA) have been applied for the optimal design of linear phase FIR low pass digital filter. In GSA, agents are considered as objects and their performance is measured by their masses. All these objects attract each other by gravity forces, and these forces produce a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate using a direct form of communication through gravitational forces. The heavy masses (which correspond to good solutions) move more slowly than lighter ones. This guarantees the exploitation step of the algorithm. GSA is apparently free from getting trapped at local optima and premature convergence. Extensive simulation results show the superiority and optimization efficacy of the GSA over the afore-mentioned optimization techniques for the solution of the multimodal, non-differentiable, and constrained filter design problems.

PurposeThe purpose of this paper is to minimize the volume of straight bevel gear and to develop resistance towards scoring failure in the straight bevel gear. Two evolutionary and more advance optimization techniques were used for performing optimization of straight bevel gears, which will also save computational time and will be less computationally expensive compared to a previously used optimization for design optimization of straight bevel gear.Design/methodology/approachThe following two different cases are considered for the study: the first mathematical model similar to that used earlier and without any modification to show efficiency of the optimization algorithm for straight bevel gear design optimization and the second mathematical model consist of constraints on scoring and contact ratio along with other generally used design constraints. Real coded genetic algorithm (RCGA) and accelerated particle swarm optimization (APSO) are used to optimize the straight bevel gear design. The effectiveness of the algorithms used has been validated by comparing the obtained results with previously published results.FindingsIt has been found that APSO and RCGA outperform other algorithms for straight bevel gear design. Optimized design values have reduced the scoring effect significantly. The values of the contact ratio obtained further enhances the meshing operation of the bevel gear drive by making it smoother and quieter.Originality/valueLow volume is one of the essential requirements of gearing applications. Scoring is a critical gear failure aspect that leads to the broken tooth in both high speed and low-speed applications of gears. The occurrence of scoring is hard to detect early and analyse. Scoring failure and contact ratio have been introduced as design constraints in the mathematical model. So, the mathematical model demonstrated in this paper minimizes the volume of the straight bevel gear drive, which has been very less attempted in previous studies, with scoring and contact ratio as some of the important design constraints, which the objective function has been subjected to. Also, two advanced and evolutionary optimization algorithms have been used to implement the mathematical model to reduce the computational time required to attain the optimal solution.

Due to the high energy consumption of buildings, there is a demand for both economically and environmentally effective designs for building energy system retrofits. While multi-objective optimization can be used to solve complicated problems, its use is not yet widespread in the industry. This study first aims to develop an efficient and applicable multi-objective building energy system optimization method, used to dimension energy production and storage retrofit components in a case campus building in Lahti, Finland. Energy consumption data of the building are obtained with a dynamic energy model. The optimization model includes economic and environmental objectives, and the approach is found to function satisfactorily. Second, this study aims to assess the feasibility and issues of multi-objective single-building energy system optimization via the analysis of the case optimization results. The results suggest that economically beneficial local energy production and storage retrofits could not always lead to life cycle CO2-eq emission reductions. The recognized causes are high life cycle emissions from the retrofit components and low Nordic grid energy emissions. The performed sensitivity and feasibility analyses show that correctness and methodological comparability of the used emission factors and future assumptions are crucial for reliable optimization results.