A comparison between optimization algorithms applied to synchronization of bilateral teleoperation systems against time delay and modeling uncertainties

A novel trajectory optimization technique has been proposed in this paper to generate reference trajectory for Satellite Launch Vehicle (SLV). For this purpose, a suitable objective function has been formulated subsuming parameters like height, velocity, injection angle and payload mass. The contribution of these parameters in evolving the objective function has been represented by assigning different weighting factors against the different parameters. Moreover, optimal selection of the setting of parameters has also been achieved by proper optimization of the weighting factors associated with the objective function. In the next step, an attempt has been made to optimize the objective function using the existing Differential Evolution (DE) scheme. As an enhancement of this work, a novel Self Adaptive Differential Evolution (SADE) algorithm has been proposed to optimize the trajectory in a more effective manner. Finally, a comparative study has been presented between the proposed SADE based design and the existing DE based design.

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

The performance of differential evolution (DE) is influenced by various factors such as algorithm parameters and mutation strategies. JADE (adaptive DE with optional external archive) is recognized as one of the most successful studies on parameter control in DE. In this study, in order to improve the search efficiency of JADE, we propose to integrate two methods we have proposed. One is the method to estimate whether the population of candidate solutions is converging or moving for particle swarm optimization. The normalized distance between the population center and the best solution is used for the estimation. When the population is converging, the search mode is adjusted to enhance the convergence. When the population is moving, the search mode is adjusted to enhance the movement. The other is the method to control DE parameters for extreme individuals in order to improve the search efficiency. The methods are modified to use the former method in JADE and to reduce the interference between the two methods. The effectiveness of JADE incorporating the proposed methods is shown by solving thirteen benchmark problems.

Self Adaptive Differential Evolution (SADE) is a popular variant of Differential Evolution (DE) that has the ability to tune its internal parameter online to produce acceptable results. In this work geometry of a nine ring concentric circular antenna array (CCAA) is thinned by vanishing one or more rings from the geometry and simultaneously finding suitable radii of active rings. Care has been taken to preserve the directivity of the array as far as possible.

A hybrid differential evolution for optimal multilevel image thresholding

Differential evolution (DE), as a very powerful population-based stochastic optimizer, is one of the most active research topics in the field of evolutionary computation. Self-adaptive differential evolution (SaDE) is a well-known DE variant, which aims to relieve the practical difficulty faced by DE in selecting among many candidates the most effective search strategy and its associated parameters. SaDE operates with multiple candidate strategies and gradually adapts the employed strategy and its accompanying parameter setting via learning the preceding behavior of already applied strategies and their associated parameter settings. Although highly effective, SaDE concentrates more on exploration than exploitation. To enhance SaDE's exploitation capability while maintaining its exploration power, we incorporate local search chains into SaDE following two different paradigms (Lamarckian and Baldwinian) that differ in the ways of utilizing local search results in SaDE. Our experiments are conducted on the CEC-2014 real-parameter single-objective optimization testbed. The statistical comparison results demonstrate that SaDE with Baldwinian local search chains, armed with suitable parameter settings, can significantly outperform original SaDE as well as classic DE at any tested problem dimensionality.

For sharing the information between solutions, biogeography based optimization (BBO) procedure uses migration operator based on biogeography. Differential evolution (DE) is another well-organized, robust and most common evolutionary procedure for solving global optimization problems. We have implemented BBO with DE algorithm and proposes a new efficient approach namely BBO/DE. To generate an effective candidate solution BBO/DE hybridize the BBO with the DE effectively and the BBO migration operators are adjusted on the basis of iteration count to enhance the performance. To certify the effectiveness of our suggested approach, we have used 6 benchmark functions and applied our proposed solution. From the comparison results it can be induced that the suggested methodology is effective and efficient both in comparison to traditional BBO and DE.

Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches

Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

Biogeography-based optimisation (BBO) is a relatively new bio-inspired and population-based optimisation algorithm. It is proved itself a worthy optimisation technique to solve various nonlinear optimisation problem. In this paper, the concept of predator-prey optimisation is incorporated in BBO to increase the diversity of the population. The proposed biogeography-based predator-prey optimisation (BPPO) technique is implemented on both single-input and dual-input power system stabilisers (PSSs). The effectiveness of the proposed algorithm is demonstrated on single machine infinite bus (SMIB) system using four different types of single and dual input PSS. From the simulation study, it is revealed that the transient performance of dual-input PSS is better than single-input PSS. The performance of the proposed algorithm is evaluated over a wide range of operating condition and the simulation results of the proposed method are compared with those obtained by BBO, seeker optimisation algorithm (SOA) and bacteria foraging optimisation (BFO). The results confirm the potential and effectiveness of the proposed BPPO algorithm over SOA, BFO and BBO.

Aiming at the problem that the value of weighted matrix Q and R in linear quadratic optimal control (LQG) controller depends on the designer’s experience, adaptive differential evolution (JADE) algorithm with optional external archive is introduced to optimize the control parameters of LQG, so as to improve the performance of LQG controller. The dynamics model of 1/4 vehicle active suspension was established as the object, and the LQG objective function was constructed. The simulation experiments were carried out on B-class and C-class roads with the help of Matlab/Simulink.The experimental results show that the RMS values of the vertical acceleration of the vehicle body, the dynamic travel of the suspension and the dynamic displacement of the tire of the active suspension based on JADE optimized LQG controller under different road surfaces are reduced to a large extent compared with that of the passive suspension, which improves the stability of the vehicle and the comfort of the ride.

Chapter 10 - Constraint-based heuristic algorithms for software test generation

In this article, an efficient optimization procedure based on herding behavior of krill individuals, krill herd algorithm (KHA), for the solution of multi-objective optimal power flow (OPF) with the objective of fuel cost minimization, voltage deviation minimization and voltage stability improvement is proposed. This algorithm is based on the effect of the influence of a teacher on the output of learners in a class. The proposed KHA approach is carried out on the standard IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems to solve different single and multi-objective OPF problems. Simulation results of the proposed approach are compared to differential evolution (DE), modified DE (MDE), multi-objective DE (MODE), evolving ant direction DE (EADDE), particle swarm optimization (PSO), improved genetic algorithm (IGA), gradient method, biogeography-based optimization (BBO), PSO with inertia weight approach (PSOIWA), PSO with constriction factor approach (PSOCFA), real-coded GA (RGA), artificial bee colony (ABC), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA), interior point method (IPM) and quantum-inspired evolutionary algorithm (QEA). The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve multi-objective OPF problems. Copyright © 2014 John Wiley & Sons, Ltd.

This paper introduces a self adaptive differential evolution (SADE) and self-tuning Hybrid differential Evolution (HDE) methods for dealing with optimal reactive power dispatch aiming at power loss reduction. The optimum reactive power dispatch of power systems is to allocate reactive power control variables so that the objective function composed of power losses is minimized and the prescribed voltage limits are satisfied. The proposed method determines the optimum settings of reactive power control variables such as, generator excitation, tap changing transformers, and shunt compensation that reduces the power loss, while maintaining the voltage stability. IEEE 14-bus test system from the literature is used to exemplify the performance of the proposed methods. Numerical results show that the proposed methods are better methods than those reported in the literature.

In this paper, we address the two-stage assembly flowshop scheduling problem with a weighted sum of makespan and mean completion time criteria, known as bicriteria. Since the problem is NP-hard, we propose heuristics to solve the problem. Specifically, we propose three heuristics; simulated annealing (SA), ant colony optimization (ACO), and self-adaptive differential evolution (SDE). We have conducted computational experiments to compare the performance of the proposed heuristics. It is statistically shown that both SA and SDE perform better than ACO. Moreover, the experiments reveal that SA, in general, performs better than SDE, while SA consumes less CPU time than both SDE and ACO. Therefore, SA is shown to be the best heuristic for the problem.