Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems

This paper proposes JADEGBO, a hybrid gradient-based metaheuristic for solving complex single- and multi-constraint engineering design problems as well as cost-sensitive security optimisation tasks. The method combines Adaptive Differential Evolution with Optional External Archive (JADE), which provides self-adaptive exploration through p-best mutation, an external archive, and success-based parameter learning, with the Gradient-Based Optimiser (GBO), which contributes Newton-inspired gradient search rules and a local escaping operator. In the proposed scheme, JADE is first employed to discover promising regions of the search space, after which GBO performs an intensified local refinement of the best individuals inherited from JADE. The performance of JADEGBO is assessed on the CEC2017 single-objective benchmark suite and compared against a broad set of classical and recent metaheuristics. Statistical indicators, convergence curves, box plots, histograms, sensitivity analyses, and scatter plots show that the hybrid typically attains the best or near-best mean fitness, exhibits low run-to-run variance, and maintains a favourable balance between exploration and exploitation across rotated, shifted, and composite landscapes. To demonstrate practical relevance, JADEGBO is further applied to the following four well-known constrained engineering design problems: welded beam, pressure vessel, speed reducer, and three-bar truss design. The algorithm consistently produces feasible high-quality designs and closely matches or improves upon the best reported results while keeping computation time competitive.

In this paper, a novel and efficient real-coded genetic algorithm (RCGA) for process optimization is developed. The proposed RCGA is equipped with Ranking Selection (RS), Direction-Based Crossover (DBX) and Dynamic Random Mutation (DRM) operators. The RS operator is used to eliminate the bad solutions and reproduce good solutions, making the whole population to achieve a better average fitness. The DBX operator uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. The DRM operator prevents the premature convergence of RCGA and at the same time increases the precision of the searched solution. The effectiveness and application of the proposed RCGA are demonstrated through a variety of single objective optimization benchmark problems. For comparative study, other existing RCGAs with different evolution operators are also performed to the same problem set. Extensive experiment results reveal that the proposed RCGA provides a significantly faster convergence speed and much better search performance than comparative methods.

Time domain electromagnetic (TDEM) is a non-invasive geophysical method. This method is an active method that makes use of the electromagnetic wave properties so that the conductivity of the lithology in the subsurface can be derived. Inversion of TDEM data was usually calculated using derivatives of least square method. These methods have drawbacks, such as the results depend on good starting models, and the final results are sometimes stuck in a local minimum, instead of global minimum. These drawbacks can be overcome by using global optimization approach, such as Monte Carlo, simulated annealing, and genetic algorithm. However, these global optimization methods need long calculation time. Because of that, this research tries to combine these two approaches as a hybrid optimization method. The hybrid optimization method is combination of conjugate gradient (CG) method and very fast-simulated reannealing (VFSA) method. The algorithm was applied to invert several synthetic models of TDEM data. Noise was added to the synthetic data to test the capabilities of hybrid optimization algorithm. This combination method was the most efficient method when compared to the conjugate gradient or simulated annealing method separately. Levenberg-Marquardt and genetic algorithm inversion results of Volvi Basins TDEM data were compared with the hybrid optimization algorithm results. The hybrid optimization results were better than Levenberg-Marquardt results, and when compared to the genetic algorithm processes, the calculation times were faster.

A simulation-based modified backtracking search algorithm for multi-objective stochastic flexible job shop scheduling problem with worker flexibility

The perfomance of neighborhood searching of GA with general mutation operator is not satisfiable. In the paper, an improved mutation operator is presented. After using GA with an improved mutation operator, the model of optimization of delivery route with single distribution center and simglevehicle is established. Standard test data are used for simulation. The proportional selection operator, tournament selection operator and truncation selection operator in GA are compared, and the result is that by using truncation selection operator, a better optimal effect can be obtained. A dynamic switching mutation operator is also proposed. It is based on 3-opt mutation operator, sub-router exchange mutation operator and two point exchange mutation operator. Simulation results show that the dynamic switching mutation operator can enhance the ability of the neighborhood searching, and GA using dynamic switching mutation operator can get a relatively stable result.

Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies

Piranha predation optimization algorithm (PPOA) for global optimization and engineering design problems

Hybrid optimization for multiple-impulse reconfiguration trajectories of satellite formation flying

In this paper, a novel hybrid optimization method to solve the phasor measurement unit (PMU) placement problem is presented. The optimal PMU placement (OPP) problem is formulated to guarantee both full observability of the network and minimal number of PMUs. In the hybrid optimization method, the mechanism of simulated annealing (SA) is involved into original particle swarm optimization (PSO) to prevent the method from premature convergence; crossover and mutation operators are applied to increase the diversity of swarm. The proposed method keeps the advantage of PSO to find the optimum result quickly, and is still simple to implement, and can further improve the ability of jumping out of local optimum results. Simulation results on several IEEE systems show the effectiveness of the method. (5 pages)

Butterfly optimisation algorithm (BOA) is a newcomer in the family of nature inspired optimisation algorithms. Although it is an effective algorithm, still, like other population-based optimisation algorithms, it encounters two probable problems: 1) entrapment in local optima; 2) slow convergence speed. In order to increase the potential of the algorithm, it is hybridised with an efficient algorithm, differential evolution (DE), which accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In this paper, a novel hybrid algorithm based on BOA and DE, namely BOA/DE is proposed to solve numerical optimisation problems. The proposed algorithm has advantages of both BOA and DE which enable the algorithm to balance the tradeoff between exploration and exploitation which produces efficient results. Engineering design problem and standard benchmark functions are employed to validate the proposed algorithm and according to the simulation results, the performance of the hybrid algorithm is superior to or at least highly competitive with the standard BOA and DE.

The Atomic Search Optimization (ASO) algorithm is an efficient metaheuristic algorithm inspired by molecular-based dynamics. Although it effectively solves numerous engineering design optimization problems, the algorithm still has drawbacks, such as premature convergence and an imbalance between exploration and exploitation. This paper proposes an improved Atomic Search Optimization Algorithm (TGD_ASO) with a genetic mutation mechanism, golden sine strategy, and chaotic mapping. Firstly, the Tent chaotic mapping is introduced to improve the population diversity of the algorithm, thereby enhancing the search space. Secondly, the golden sine strategy is utilized to strengthen global search efficiency. After that, we introduce the random variation and crossover operations in differential evolutionary algorithms to perturb the updated positions to help the atoms jump out of the local optimum and improve the population’s diversity and the algorithm’s accuracy. To validate and test the performance of the proposed TGD_ASO algorithm. We analyze the performance of the TGD_ASO algorithm on the CEC2017 benchmarks and six real design issues. In the CEC2017 benchmark test, the algorithm achieved 23 and 22 top positions in the 30 and 50 dimensions, respectively. Its performance significantly surpassed that of the ASO algorithm. Experimental data applied to engineering design problems have shown that the algorithm achieved 5 key outcomes. The algorithm also significantly outperforms metaheuristic algorithms such as ASO in terms of convergence time, accuracy, stability, and optimization ability.

This paper presents a hybrid optimization approach for the enhancement of performance of a piezoelectric energy harvesting system (PEHS). The existing PEHS shows substantial power loss during hardware implementation. To overcome the problem, this study proposes a hybrid optimization technique to improve the PEHS efficiency. In addition, the converter design as well as controller technique are enhanced and simulated in a MATLAB/Simulink platform. The controller technique of the proposed structure is connected to the converter prototype through the dSPACE DS1104 board (dSPACE, Paderborn, Germany). To enhance the proportional-integral voltage controller (PIVC) based on hybrid optimization method, a massive enhancement in reducing the output error is done in terms of power efficiency, power loss, rising time and settling time. The results show that the overall PEHS converter efficiency is about 85% based on the simulation and experimental implementations.

Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

We introduce AHA–SCA, a compact hybrid optimiser that alternates the wave-based exploration of the Sine–Cosine Algorithm (SCA) with the exploitation skills of the Artificial Hummingbird Algorithm (AHA) within a single population. Even iterations perform SCA moves with a linearly decaying sinusoidal amplitude to explore widely around the current best solution, while odd iterations invoke guided and territorial hummingbird flights using axial, diagonal, and omnidirectional patterns to intensify the search in promising regions. This simple interleaving yields an explicit and tunable balance between exploration and exploitation and incurs negligible overhead beyond evaluating candidate solutions. The proposed approach is evaluated on the CEC2014, CEC2017, and CEC2022 benchmark suites and on several constrained engineering design problems, including welded beam, pressure vessel, tension/compression spring, speed reducer, and cantilever beam designs. Across these diverse tasks, AHA–SCA demonstrates competitive or superior performance relative to stand-alone SCA, AHA, and a broad panel of recent metaheuristics, delivering faster early-phase convergence and robust final solutions. Statistical analyses using non-parametric tests confirm that improvements are significant on many functions, and the method respects problem constraints without parameter tuning. The results suggest that alternating wave-driven exploration with hummingbird-inspired refinement is a promising general strategy for continuous engineering optimisation.

Among the emerging technologies nowadays, the genetic algorithm, a powerful optimization technique, is becoming the subject of new craze among neural network researchers. Genetic algorithms (GAs) for training and designing artificial neural networks (ANNs) have proved to be a useful integration. This paper reports an improvement over earlier work on the genetic evolution of neural network weights using the two-parents multipoint restricted crossover (Double-MRX) operator proposed by Srivastava, Shukla and Srivastava (Microelectronics Journal, vol. 29, no. 11, p.921-31, 1998). In this research, a methodology to improve network convergence is presented by introducing a new concept contrary to natural law, i.e. crossover with randomly selected multiple crossover sites restricted to lie within individual weight boundaries, hence termed as Triple-MRN. In GAs, the search strategy relies more on exchange of information between individual building blocks by exploiting crossover operator. The use of Triple-MRX promotes cooperation among individuals, that better exploits the new genotypic information contained in genome variation. This ensures a much more effective search, both in terms of quality of the solution and speed of convergence as shown by the simulation experiments. Fitness function used in the authors' study is 1/MSE (mean square error). The effectiveness of the proposed technique is tested by evaluating the capability of neural network to learn a real-world gas identification problem.