Intelligent Frequency Assignment Algorithm Based on Hybrid Genetic Algorithm

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem, as it is classified as NP-hard problem and therefore no deterministic algorithms can solve them in a reasonable amount of time. The main objective of solving this problem is to find suitable job sequences on machines to optimize the performance criteria. In this paper, a meta-heuristic approach for solving the job-shop scheduling problem (JSSP) is presented. This approach uses a hybrid genetic algorithm that is suggested by previous stuody, to generate the best solutions and then a simulated annealing algorithm to improve the quality and performance of the best solutions to produce the optimal/near-optimal solution. Ten benchmark problems adopted from the previous study are used to evaluate the performance of the proposed algorithm. The computational results validate the quality of the proposed algorithm; this is done by calculating the completion time Cmax

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

This paper proposed and implemented an genetic and weed invasion hybrid algorithm in Sparse Array Synthesis. The classical weed invasion algorithm has high requirements on the convergence parameters in the process of generating weeds from seeds. Inappropriate parameters can easily lead to local convergence of evolution. The mutation and crossover operations similar to the genetic algorithm were introduced into the classical weed invasion algorithm. The method improves convergence speed, avoids local convergence, and enhances the stability of the algorithm. The algorithm is used in practical application in the synthesis of linear sparsely distributed array pattern, and the predetermined index is obtained through calculation. At the same time, compared with the genetic algorithm and the classical weed invasion algorithm, the results show that the hybrid algorithm has certain advantages.

Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

PurposeThe paper aims to present a new methodology for hybrid genetic algorithms (GA) in the solution of electromagnetic optimization problems.Design/methodology/approachThis methodology can be seen as a local search operator which uses local quadratic approximations for each objective and constraint function in the problem. In the local search phase, these approximations define an associated local search problem that is efficiently solved using a formulation based on linear matrix inequalities.FindingsThe paper illustrates the proposed methodology comparing the performance of the hybrid GA against the basic GA in two analytical problems and in the well‐known TEAM benchmark Problem 22. For the analytical problems, 30 independent runs for each algorithm were considered whereas for Problem 22, ten independent runs for each algorithm were taken.Research limitations/implicationsFor the analytical problems, the hybrid GA enhanced both the convergence speed, in terms of the number of function evaluations, and the accuracy of the final result. For Problem 22, the hybrid GA was able to reach a better solution, with a better value of the standard deviation with less CPU time.Practical implicationsThe paper could be useful both for device designers and researchers involved optimization in computational electromagnetics.Originality/valueThe hybrid GA proposed enhanced the convergence speed, in terms of the number of function evaluations, representing a faster and robust algorithm for practical optimization problems.


 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.

Equivalent circuit model of induction motors was established and the formulas of power losses were given. Motor efficiency was optimized by Genetic Algorithm(GA) and Hybrid Genetic Algorithm(HGA). Power factor, breakdown torque ratio, locked-rotor torque ratio and locked-rotor current ratio were regarded as constraints and the optimal result is satisfying. Genetic Algorithm and Pattern Search Algorithm are integrated into a composite algorithm-Hybrid Genetic Algorithm. Motor efficiency rises further through Hybrid Genetic Algorithm.

Many applications use a combination of a local optimum search (LOS) and a genetic algorithm (GA), called a hybrid genetic algorithm (HGA), to solve problems. This hybrid can improve the performance of finding optimum solutions, but the HGA may produce redundancy when applying an LOS to inappropriate populations. This redundancy is the cause of high computation time, and it generates premature convergence and decreases HGA performance. This research therefore aims to reduce redundant LOS in HGAs. We propose a new technique called diversity selection (DS) and measure redundancy when applying an LOS in HGA. In this work, the DS selects appropriate populations to which to apply an LOS. The experiment then compares DS with other HGAs on numerical optimization problems. In addition, the HGAs were tested on two LOSs, a Nelder-Mead method and a quasi-Newton method to compare the speed of finding the optimum point in an LOS. The experimental results show that DS was able to quickly find the optimum point and give fewer redundant LOSs than other HGAs.

The multiobjective time-dependent route planning problem is a hard multiobjective combinatorial optimization problem. Metaheuristics showed success in solving many hard optimization problems and recently many efforts have been directed to hybridize elements from different metaheuristics and search methods. The hybridization of genetic algorithms and local search methods proved to be successful in many domains. In this paper we present a genetic local search algorithm for solving the multiobjective time-dependent route planning problem taking the multiobjective route planning in dynamic multihop ridesharing as an example problem. The behavior of the proposed algorithm is compared, on two problem instances using a set of widely used quality indicators, with the behavior of a genetic algorithm proposed for solving the same problem. Experimentation results indicated that the proposed algorithm outperforms the genetic algorithm regarding all quality indicators.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

The paper discusses a hypergraph-based polynomial time algorithm for constructing a DNA sequence corresponding to a given spectrum with no errors. The presence of n-ary relations in the expression of DNA sequencing led to the advent of hypergraphs in DNA sequencing. An efficient algorithm, unimodular hypergraph (UMHG), has been proposed for constructing unimodular hypergraph that represents the short-read DNA sequence of the given spectrum. The performance of the proposed algorithm is evaluated in comparison with prominent algorithms such as greedy, greedy (lag), ant colony optimization(ACO), multi-level ACO, enhanced genetic algorithm (GA), hybrid GA and tabu search against 40 instances. The proposed UMHG algorithm is found to outperform the other algorithms in terms of average similarity score. UMHG is significant in terms of minimum computing time, especially as the spectrum size increases computing time decreases considerably, owing to the unimodularity property.

The objective is to minimize the production cost of the thermal units. An elegant approach is presented in order to obtain the equivalent cost function of the participating nonfuel restricted units [N. Ramaraj (1993)]. The hybrid genetic algorithm (GA) technique is used for real power optimization. Here real coded GA is used for global search and the fine tunings are done by tabu search (TS) to direct the search towards the optimal region and local optimization. The GA operator's selection, arithmetic crossover and dynamic mutation are used to generate successive sets of possible operating policies. This technique improves the quality of the solution and reduces the computation time. The optimal solution is obtained neglecting losses. The fast decoupled load flow (FDLF) analysis is conducted to find the losses by substituting the generation values. Then the loss is participated among all generating units using participation factor method. Applying the results again to the load flow, checks the voltage limit violation. The Algorithm is tested on IEEE 6-bus system and a practical 66-bus system. The proposed method is compared with the classical method. It is observed that the proposed algorithm is superior, reliable and fast.

As a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of constraints deriving from the capability of available platforms and munitions (or weapons), target characteristics, timings for operation and so on. In order to overcome the drawbacks of easily falling into premature convergence and local optimum of existing heuristic algorithms when they are employed to optimize WTA problems, this paper proposes a hybrid genetic algorithm (HGA) which combines an adaptive genetic algorithm (AGA) with an adaptive variable neighborhood search algorithm (AVNSA) to balance the exploration and exploitation ability. In the framework of HGA, AGA is used for wide scope search in the solution space to avoid premature convergence while AVNSA for local search to jump out of the local optimal space. The simulation result demonstrates the effectiveness and feasibility of the proposed HGA in small-scale WTA optimization problems and through a comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm in large-scale WTA optimization problems are revealed.