Formulation Of Fitness Function Research Articles

Implementation of Genetic Algorithm on Vehicle Routing System

Abstract: This study explores the use of Genetic Algorithms (GAs) to solve the Vehicle Routing Problem (VRP) in logistics. GAs, inspired by evolutionary principles, prove effective in finding efficient routes for a fleet of vehicles serving customers where the goal is to find the most efficient routes for a fleet of vehicles serving customers. Inspired by evolution, GAs prove effective in this task by selecting promising routes, creating new options through crossover and mutation, and evaluating fitness based on objectives and constraints. The study incorporates techniques like reparation, penalties, and adaptive tuning to boost GA performance. Real-world applications in transportation and logistics demonstrate substantial cost savings. The key takeaway is the crucial role of parameter selection, representation design, and fitness function formulation in the success of GAs in solving VRP. The combination of GAs and VRP resolution shows promise in optimizing logistics, improving efficiency, and enhancing service quality. In simpler terms, the study explores a smart way, inspired by nature, to figure out the best routes for delivery trucks, leading to significant cost savings and better service.

Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

SummaryClustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real‐life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade‐off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.

A stochastic computational intelligent solver for numerical treatment of mosquito dispersal model in a heterogeneous environment

In this paper, the design of stochastic computational intelligent solver is presented for the solution of mathematical model representing the dynamics of mosquito dispersal in a heterogeneous environment by feedforward artificial neural networks (FFANNs) trained with genetic algorithms (GAs) aided with sequential quadratic programming (SQP), i.e., FFANN-GASQP. In the scheme FFANN-GASQP, the formulation of fitness function in mean square error sense with continuous mapping-based differential equation models of FFANNs for the mosquito dispersal system and training of these networks are accomplished by integrated competency of GA and SQP. The exactness, reliability and stability of the designed FFANN-GASQP approach are established through comparative studies with Adams numerical results for both single and multiple runs. Outcomes of statistical assessments are used to validate the accuracy and convergence of the designed FFANN-GASQP scheme.

Design of backtracking search heuristics for parameter estimation of power signals

This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.

Development of seven hybrid methods based on collective intelligence for solving nonlinear constrained optimization problems

Many real-world problems can be seen as constrained nonlinear optimization problems (CNOP). These problems are relevant because they frequently appear in many industry and science fields, promoting, in the last decades, the design and development of many algorithms for solving CNOP. In this paper, seven hybrids techniques, based on particle swarm optimization, the method of musical composition and differential evolution, as well as a new fitness function formulation used to guide the search, are presented. In order to prove the performance of these techniques, twenty-four benchmark CNOP were used. The experimental results showed that the proposed hybrid techniques are competitive, since their behavior is similar to that observed for several methods reported in the specialized literature. More remarkably, new best known are identified for some test instances.

A Statistical Scheme to Support Performance Comparisons between Different Designs of Evolutionary Algorithms

Evolutionary algorithms are stochastic heuristics which can optimize over special functions, known as fitness functions, by manipulating the structure of candidate solutions known as individuals. Multi-objective evolutionary algorithms can deal with many objectives to be optimized, whether concurrent or divergent, ending by returning an optimal frontier, i.e. a set of solutions all defined as Pareto optimal. The idea behind using evolutionary algorithms to perform computer automated design is to be able to formulate a fitness function that, starting from a candidate solution, could reflect the impact the evaluated individual has on those objectives to be optimized. The case study presented in this work is an application for computer automated exterior lighting design which has some concurrent objectives to be optimized: the energy efficiency and the illumination quality. This work investigates four metrics to illumination quality and two metrics for energy efficiency as possible proposals to the fitness function formulation. Eight variations were designed as combinations of pairs from those metrics. To help in the decision process, the statistical hypothesis test known as difference of means is then used to enable comparisons between those variations. This test is performed two by two and three decision matrices is then derived, the ones about global uniformity, mean electrical power, and mean efficiency class index. The concept of Pareto's “statistical dominance”, defined in this work and based on statistical evidences, indicates a final decision about which one from the previous designed variations of fitness function is the more appropriated for the presented problem.

Minimizing Energy Consumption in Scheduling of Dependent Tasks using Genetic Algorithm in Computational Grid

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

Application of Genetic Algorithm for Optimization of Important Parameters of Magnetically Biased Microstrip Circular Patch Antenna

The application of Genetic Algorithm (GA) to the optimization of important parameters (Directivity, Radiated Power, Impedance etc.) of magnetically biased microstrip antenna, fabricated on ferrite substrate, is reported. The fitness functions for the GA program have been developed using cavity method for the analysis of microstrip antenna. The effect of external magnetic biasing has also been incorporated in the fitness function formulation as effective propagation constant. Using stochastic based search method of GA the common characteristics of electro-magnetic were entertained which cannot be handled by other optimization techniques. The genetic algorithm was run for 500 generations. The computed results are in good agreement with the results obtained experimentally.

Development of CFRP racing motorcycle rims using a heuristic evolutionary algorithm approach

The scope of this paper is the application of evolutionary optimization methods to the development of composite fibre reinforced plastics (CFRP) racing motorcycle rims. The mass and the moment of inertia of a front and a rear CFRP rim are minimized subject to manufacturing, strength, and stiffness constraints. The stacking sequence of the composite laminates is optimized by applying a sophisticated parameterization concept making an excellent compromise between a huge variety of structure properties and a reasonable number of optimization parameters. The mechanical properties are simulated using the finite element analysis package ANSYS . Resulting displacement and Tsai–Wu index values are combined with the mass of the rim in order to assign a fitness value to each different design solution. The smart formulation of the fitness function allows the exploration of solutions close to the required strength and stiffness properties. The proprietary software DynOPS is utilized as an optimization engine. It links an evolutionary algorithm to arbitrary simulation programs and controls the entire optimization process. The sophisticated parameterization concept, together with the fitness function formulation, are the basis for the development of CFRP motorcycle rims decisively lighter than state-of-the-art magnesium alloy rims.

Optimum Design Using Radial Basis Function Network and Adaptive Range Genetic Algorithms. 3rd Report. Usage of Data Generation by Using Self-Organizing Maps.

Genetic Algorithms have been studied widely to report their effectiveness in a variety of fields. As optimizer, they give us much privilege in considering types of design variables for multi-peaked problems. However, there exist some shortcomings, such as treatment of continuous variables, formulation of fitness function and the number of function calls. In the previous studies, we have been developed Adaptive Range GAs for continuous variable problem, by using RBF (Radial Basis Function) networks as approximation tools in solving large scale constraint optimization problems. From these results, we have shown the effectiveness to obtain good results. However, it is very difficult to give data within feasible region only by using random numbers for RBF and almost 80% of them are consumed in vain. In order to raise their ratio, we introduce SOM (Self-Organizing Map) as classification tools to choose data within feasible region. In this study, we will show the effectiveness of the proposed method based upon a famous benchmark test problem.