Conventional Genetic Algorithm Research Articles

Development of an Adaptive Genetic Algorithm to Optimize the Problem Of Unequal Facility Location

Abstract The problem of unequal facility location involves determining the location of a set of production equipment whose dimensions are different, as well as the interrelationships between each of them. This paper presents an efficient method for optimizing the problem of unequal facility layouts. In this method, the genetic algorithm is improved and developed into an adaptive genetic algorithm. In this algorithm, the mutation operator is applied only when the similarity of chromosomes in each population reaches a certain level. This intelligence prevents jumps in situations where they are not needed and reduces computational time. In order to measure the performance of the proposed algorithm, its performance is compared with the performance of conventional genetic algorithms and refrigeration simulators. Computational results show that the adaptive genetic algorithm is able to achieve higher-quality solutions.

GMPPT approach for photovoltaic systems under partial shading conditions using a genetic algorithm

In this paper a new global maximum power point tracking (GMPPT) approach is proposed for a photovoltaic (PV) module using a genetic algorithm (GA) system under partial shading conditions. The partial shading condition is one of the adverse phenomena that the PV system experiences, it is difficult to track true peaks because of existence of multiple peaks in a PV array (partially shaded based). The conventional GMPPT algorithms met in the literature, the initial value of reference is approximated arbitrarily for various conditions of irradiation and temperature and they can’t distinguish between the local and global peaks if partial shading occurs, what reduces the performances of the tracking of the point of optimal operation of PV systems. So, to improve these performances, we proposed a nonlinear based GMPPT method to estimate the initial optimal operating point. To compare with conventional GAs methods, the global peak (GP) tracking process is accomplished after follow the far fewer power perturbation steps. This approach made a possible to largely improve the GP tracking in the PV system, improve the accuracy of the GMPPT algorithms, and accelerate the convergence speed. This GMPPT method is checked for different shading profiles through the simulation and verification.

Multi objective ant Colony Optimisation to obtain efficient metro speed profiles

Obtaining efficient speed profiles for metro trains is a multi-objective optimisation problem where energy consumption and travel time must be balanced. Automatic Train Operation (ATO) systems may handle a great number of possible speed profiles; hence optimisation algorithms are required to find efficient ones in a timely manner. This paper aims to assess the performance of a particular meta-heuristic optimisation algorithm, a variation of the traditional Ant Colony (ACO) modified to deal with multi-objective problems with continuous variables: MOACOr. This algorithm is used to obtain efficient speed profiles in up to 32 interstation sections in the metro network of Valencia (Spain), and the convergence and diversity of these solution sets is evaluated through metrics such as Inverse Generational Distance (GD) and Normalised Hypervolume (NH). The results are then compared to those obtained with a conventional genetic algorithm (NSGA-II), including a statistical analysis to identify significant differences. It has been found that MOACOr shows a better performance than NSGA-II in terms of convergence, regularity and diversity of the solution. These results indicate that MOACOr is a good alternative to the widely used genetic algorithm and could be a better tool for rail operation managers trying to improve energy efficiency.

Applying GA-VNS approach to supply chain network model with facility and route disruptions

ABSTRACT In this paper, a supply chain network (SCN) model which simultaneously considers the disruption risks of facility and route is proposed. As most of conventional studies have focused either on facility disruption solely or on route disruption solely, simultaneously considering the disruption risks of facility and route in the SCN model can reinforce the efficiency and stability for its implementation. The SCN model with the disruption risks is formulated as a nonlinear 0–1 integer programming model, and a hybrid metaheuristic (pGA-VNS) approach which combines genetic algorithm (GA) with variable neighborhood search (VNS) is used for solving the nonlinear 0–1 integer programming model. In numerical experiment, various-sized SCN models with the disruption risks at each stage are presented and they are used for comparing the performance of the pGA-VNS approach with those of some conventional approaches (GA and VNS as single metaheuristic approaches and various GA-VNSs as hybrid metaheuristic approaches). Experimental results show that the pGA-VNS approach outperforms conventional GA, VNS and GA-VNS approaches, and the efficiency and stability of the SCN model with the disruption risks are also proved.

Structural optimization of metamaterials based on periodic surface modeling

Metamaterials are engineered microstructures or composites which exhibit novel properties that are not found in nature. Structural optimization methods for shape and topology optimization have been developed in designing metamaterials. The computational cost of the traditional topology optimization methods is high due to the high-dimensional design space. Shape optimization and topology optimization are also performed separately. To improve the search efficiency, a new structural optimization method based on periodic surface modeling is proposed to simultaneously optimize the shape and topology of metamaterials. A wide variety of topology can be represented with a small number of parameters in the periodic surface models. Therefore, the search space is significantly reduced. A mixed-integer Bayesian optimization method is also developed with a new Gaussian process kernel, which incorporates both discrete and continuous design parameters in the periodic surface models. Both the mixed-integer Bayesian optimization method and conventional population-based genetic algorithms are used to solve the structural optimization problem. The design of mechanical metamaterials with high strength–weight ratio and negative Poisson’s ratio is demonstrated.

Modeling combined economic emission dispatch for renewable energy system via Levy-based glowworm swarm optimization

PurposeCurrently, more renewable energy resources with advanced technology levels are incorporated in the electric power networks. Under this circumstance, the attainment of optimal economic dispatch is very much essential by the power system as the system requires more power generation cost and also has a great demand for electrical energy. Therefore, one of the primary difficulties in the power system is lowering the cost of power generation, which includes both economic and environmental costs. This study/paper aims to introduce a meta-heuristic algorithm, which offers an solution to the combined economic and emission dispatch (CEED).Design/methodology/approachA novel algorithm termed Levy-based glowworm swarm optimization (LGSO) is proposed in this work, and it provides an excellent solution to the combined economic and emission dispatch (CEED) difficulties by specifying the generation of the optimal renewable energy systems (RES). Moreover, in hybrid renewable energy systems, the proposed scheme is extended by connecting the wind turbine because the thermal power plant could not control the aforementioned costs. In terms of economic cost, emission cost and transmission loss, the suggested CEED model outperforms other conventional schemes genetic algorithm, Grey wolf optimization, whale optimization algorithm (WOA), dragonfly algorithm (DA) and glowworm swarm optimization (GSO) and demonstrates its efficiency.FindingsAccording to the results, the suggested model for Iteration 20 was outperformed GSO, DA and WOA by 23.46%, 97.33% and 93.33%, respectively. For Iteration 40, the proposed LGSO was 60%, 99.73% and 97.06% better than GSO, DA and WOA methods, respectively. The proposed model for Iteration 60 was 71.50% better than GSO, 96.56% better than DA and 95.25% better than WOA. As a result, the proposed LGSO was shown to be superior to other existing techniques with respect to the least cost and loss.Originality/valueThis research introduces the latest optimization algorithm known as LGSO to provide an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. To the best of the authors’ knowledge, this is the first work that utilizes LGSO-based optimization for providing an excellent solution to the CEED difficulties by specifying the generation of the optimal RES.

Application of Monte Carlo stochastic optimization (MOST) to deep learning

In this paper, we propose a new optimization method based on the Monte Carlo method. The proposed method is applied to several benchmark problems, and the result of applying it to the optimization of neural network is reported. Deep machine learning using neural networks is one of the important keywords to promote innovation in today’s advanced information society. Therefore, research on large-scale, high-speed, and high-precision algorithms has been actively conducted. The author has developed an optimization method in which the search region for multivariate parameters constituting the objective function is divided into two regions for each parameter, the integral values of each divided region are numerically calculated by the Monte Carlo method, the magnitude of each integral value is compared, and the optimum point is judged to be in a small region. The proposed method was applied to 50 variable benchmark functions (Schwefel and Ackley Functions), and was compared with the results of genetic algorithm (GA) which was a representative of existing optimization methods. As a result, it was confirmed that the proposed method is faster and more accurate than GA. In addition, the proposed method is applied to machine learning by neural networks, specifically XOR gate circuits and IRS classification problems, and verified. The neural network optimized by MOST reproduced teacher data and test data faster and more accurately than conventional Adam and genetic algorithms.

Micro Genetic Algorithm to Solve Economic Dispatch in IEEE 30 Bus Transmission System

To produce electric power in a power system, it is necessary to find a way to keep the cost of generator fuel consumption or operating costs of the entire system to a minimum by determining the combined output power of each generating unit under the constraints of the system load demands and the limits of the generation capacity of each generating unit. . This method is known as economic generation. The researcher usually using conventional method such us Lagrange and more modern methods, Artificial Intelligence, such us Particle Swarm Optimization (PSO), Genetic Algorithm (GA) to solve economic dispatch problem. The newest technology in Artificial Intelligence had invented a new method to solve economic dispatch, it’s named micro-Genetic Algorithm (µ-GA). In this paper, µ-GA is applied to solve economic dispatch in IEEE 30 Bus Transmission System. Then the result will be compared by Lagrange and conventional GA. Thus the simulation show that µ-GA has better result than Lagrange and conventional GA. In IEEE 30 Bus System, μ-GA method results generation cost of 568.80 $/h with the generated power of 189.45 MW and system losses of 0.25 MW in 30th generation. It is 2.64% cheaper than the GA method and 1.15% cheaper than the Lagrange method. Finally, it can be said that in the case of economic dispatch in IEEE 30 Bus Transmission System, the μ-GA method is the best method compared to the Lagrange and GA methods.

Thermal Aeroelastic Tailoring for Laminated Panel with Lamination Parameters and JAYA Algorithm

This work investigates thermal aeroelastic tailoring of a laminated composite panel with a lamination parameter-based method. Equivalent membrane and bending coefficients of thermal expansions for symmetric laminated panel are derived and represented with lamination parameters using Classical Laminated Plate Theory. The relationship between thermal flutter behavior and lamination parameters is examined. The optimization process is split into two stages. In the first stage, lamination parameters and laminate thicknesses are as design variables to minimize the structure mass, subject to thermal flutter behavior and feasible region constraints of lamination parameters. In the second-stage, instead of using conventional genetic algorithm, the enhanced JAYA method is extended to search the laminate configuration to target the optimal lamination parameters. The effectiveness of the presented method is demonstrated through a thermal aeroelastic tailoring problem.

Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

For the sustainability of power supply and operation systems, planners aim to deliver power at an optimum value to consumers, while maintaining stability in the system. The load-shedding approach has proven to be an effective means of achieving the desired stability. This paper presents a nodal analysis to establish critical bus identification in the power grid. A power simulation for load shedding was created using the power system analysis toolbox (PSAT) for identifying and isolating weak buses on the power system. A computational algorithm was developed using differential evolution (DE) for minimizing service interruptions and blackouts, and was tested against the conventional genetic algorithm (GA). The proposed algorithm was implemented on an IEEE 30-bus test system. The simulation results were analyzed before and after the application of DE. It was observed that after the application of DE, load shedding gives an efficient result of 10.6%, 8.7%, and 13.4% improvement at buses 26, 29, and 30, respectively, after being tested using a genetic algorithm (GA), with a result of 10.2%, 7.6% and 13.1% on the same respective buses. This work will further expand the reliability and availability of power systems toward a sustainable, steady power supply that is void of nodal or bus cutoffs.

GAs with escape from stagnation of search for multiset iteration permutations problem – consecutive meals planning

AbstractIn this paper, consecutive meals planning is formulated as a multiset iteration permutation problem that determines the optimal meals plan on a period consisting of consecutive days. In this problem, a meal is characterized by some characteristics such as food style, ingredient, cooking method, and so on. The evaluation function is defined by use of information entropy for measuring the appearance order of meal's characteristics on the meals plan. For optimizing this problem, Genetic Algorithms (GAs) with escape from stagnation of search are proposed. It is empirically shown that the proposed GAs with escape operation work better than the conventional permutation GA without escape operation for small and large size optimization problems.

Optimal Placement of Renewable Energy Generators Using Grid-Oriented Genetic Algorithm for Loss Reduction and Flexibility Improvement

Optimal planning of renewable energy generator (REG) units helps to meet future power demand with improved flexibility. Hence, this paper proposes a grid-oriented genetic algorithm (GOGA) based on a hybrid combination of a genetic algorithm (GA) and a solution using analytical power flow equations for optimal sizing and placement of REG units in a power system network. The objective of the GOGA is system loss minimization and flexibility improvement. The objective function expresses the system losses as a function of the power generated by different generators, using the Kron equation. A flexibility index (FI) is proposed to evaluate the improvement in the flexibility, based on the voltage deviations and system losses. A power flow run is performed after placement of REGs at various buses of the test system, and system losses are computed, which are considered as chromosome fitness values. The GOGA searches for the lowest value of the fitness function by changing the location of REG units. Crossover, mutation, and replacement operators are used by the GOGA to generate new chromosomes until the optimal solution is obtained in terms of size and location of REGs. A study is performed on a part of the practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India for the base year 2021 and the projected year 2031. Load forecasting for the 10-year time horizon is computed using a linear fit mathematical model. A cost–benefit analysis is performed, and it is established that the proposed GOGA provides a financially viable solution with improved flexibility. It is established that GOGA ensures high convergence speed and good solution accuracy. Further, the performance of the GOGA is superior compared to a conventional GA.

Partial Gaussian-Approximation Soft Demapper for the Core Layer of MIMO-LDM in ATSC 3.0

In this paper, we propose a new partial Gaussian approximation (PGA) soft demapper for the core layer (CL) signal in the multiple-input-multiple-output layered-division-multiplexing (MIMO-LDM) of ATSC 3.0 systems. Unlike a conventional GA demapper, where enhanced layer (EL) signals are intentionally modelled as additive Gaussian noise, the proposed PGA jointly decodes the CL signal by assuming the EL signals to be QPSK signals added with the Gaussian noise irrespective of their modulation order and type. The assumed QPSK constellations are used in such a way that the residual signal power in the EL signals should be minimized. We verify the performance gains from this new PGA demapper by analyzing the variation of the received injection-level with respect to MIMO channel gains. Simulation results show that this new PGA outperforms conventional GA at the cost of an acceptable increase in decoding complexity, especially when dealing with low injection-levels or high code rates.

Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm

When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.

An Adaptive Robustness Evolution Algorithm With Self-Competition and its 3D Deployment for Internet of Things

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology to increase resistance against malicious attacks is a complex problem, especially on 3-dimension (3D) topological deployment. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, the additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the population’s evolution. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. A self-competitive mechanism is used to ensure convergence. We construct a 3D IoT topology that is optimized by AREA. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

Applying 3DES to Chaotic Synchronization Cryptosystems

The triple data encryption standard (3DES) has played a key role in the field of data encryption over the past few decades. However, the ciphertext&#x2019;s security is decreased by the meet-in-the-middle attack. Accordingly, we propose a systematic design methodology combining chaotic synchronization with 3DES to conduct double encryption. The encryption method not only increases the strength of the communications system but also effectively protects the encrypted message (ciphertext). The conventional genetic algorithm (GA) has flaws in premature convergence and local search. While the methods of improving the GA performance have been explored, we adopted an improved genetic algorithm (IGA) in this work due to its better performance in the light of globalization and convergence rate. The IGA-based observer not only realizes the exponential synchronization but also minimizes the disturbance attenuation level to get optimal <inline-formula> <tex-math notation="LaTeX">$H^{\infty }$ </tex-math></inline-formula> performance simultaneously. In the end, an example with numerical simulations is provided to demonstrate the effectiveness of this study.

A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

Cell Division Genetic Algorithm for Component Allocation Optimization in Multifunctional Placers

Optimizing all the objectives of the printed circuit board assembly (PCBA) optimization in a multifunctional placer remains a formidable challenge till now. This article converts the original PCBA optimization problem to a newly defined component allocation problem, which decides the component-type handled by each head per pickup-and-place (PAP) cycle. The component allocation problem is a quadratic 3-D assignment problem (Q3AP) and effectively combines the optimization of all the main objectives. It is possible that one head stays idle, so the assigning 2-D locations are uncertain. We propose the cell division genetic algorithm (CDGA) to solve such a complex Q3AP. The CDGA allocates a component cell as the basic unit. Each of the first-generation component cells contains the mounting points of the same type. A cell chromosome decoding heuristic is designed to determine the next assigning head. By doing so, the problem dimension is reduced, so the conventional GA can be used for searching the optimal component allocation formed by the current-generation cells. When a better allocation can no longer be found by allocating the current cells, the cell division operation is performed to divide each cell into two new cells. The new cells are used in the next round of GA searching, which further optimizes the allocation from two perspectives: better balancing the minimization of nozzle changes and PAP cycles, more flexibly maximizing the simultaneous pickups with the uncertain locations. The CDGA works continuously until the current cells cannot bring any improvement. In simulations and experiments using the industrial samples, the proposed algorithm significantly reduces the PCBA time compared to two recent studies and the built-in optimizer of the widely used multifunctional placer, Hanwha SM482 PLUS, which demonstrates its effectiveness and superiority.

Multi-spin-flip engineering in an Ising machine

A merge process is proposed to engineer a multi-spin flip in an Ising machine. The merge process deforms the Hamiltonian (energy function) of the Ising model. We prove a theorem for the merge process and show that a single-spin flip in the deformed Hamiltonian is equivalent to a multi-spin flip in the original Hamiltonian. A merge process induces a transition within the subspace of feasible solutions. We propose a hybrid simulated annealing (SA) algorithm with the merge process. The hybrid algorithm outperforms the conventional SA algorithm, genetic algorithm, and tabu search in the binary quadratic knapsack problems (QKP) and the quadratic assignment problems (QAP). Finally, the hybrid merge process is used in a real Ising machine. The performance is improved in QKP and QAP instances. The merge process is generally applicable to existing Ising machine hardware because the deformed Hamiltonian keeps the format of the Ising model.

Discrete-time simulation for performance modelling of FIFO single-server queuing system

Optimal queue management is crucial for improving the network operation since packet loss is commonly attributed to the likelihood of buffer overflow at intermediate routers on end-to-end communication path. In this paper, a transient discrete-time simulation model is proposed based on queuing theory served by single-server system and single first-in first-out (FIFO) queue. This queuing service framework effectively enforces the best-effort service to the streams of incoming data packets across the network with disparate quality of service (QoS) requirements. We plot the network performance measures such as queue length, delay, server utilisation and packet lifetime to validate the practical wireless systems. Besides, we employ conventional multiple-objective genetic algorithm to model the trade-off among key queuing system parameters including queuing delay, packet drop rate and network bandwidth. Finally, the efficiency of our queuing optimisation model is significantly compared with previous works in terms of bandwidth utilisation, delay, and packet loss rate.