A Hybrid Leader Cooperation Algorithm for high dimention numerical optimization

This dissertation investigates methods for the automated design and optimization of laminated composite structures. Optimal design of laminated composite structures is challenging due to the possibility to locally adapt the material system to the mechanical situation. Automated structural design on a computer is enabled by a combination of numerical simulation and optimization algorithms. The finite element method provides the possibility to predict mechanical properties of virtual candidate solutions. Numerical optimization algorithms then adapt the structure’s attributes in order to meet specific demands formulated on the aforementioned simulated properties. Evolutionary algorithms are a group of biologically inspired optimization algorithms which have repeatedly and successfully been applied to optimal design problems with laminated composites. This thesis focuses on methods to compose evolutionary algorithms for the specific traits of laminate optimization problems. A special focus is set on the variation state of a canonical evolutionary algorithm. This state is particularly influenced by the genetic representation of a candidate solution, i.e. the way the adjustable attributes are translated to machine readable entities. The aim of the thesis is to develop and examine genetic representation schemes to concurrently evolve a structure’s topology, shape, and laminate properties. An overview of structural optimization and evolutionary computation illustrates the state-of-the-art. In variable-length representations, the dimensionality of the search space is a variable to optimize. The importance of variable-length representations in evolutionary topology and laminate optimization is exemplified. A weakness of established variable-length crossover operators is the treatment of length constraints. Based on existing concepts a split-and-splice variable-length crossover operator respecting length constraints is introduced. In order to improve the solution quality of a real-encoded evolutionary algorithm, a gradient-based local search is embedded in the variation state of the algorithm. The algorithm intrinsic parallelization is extended to the variation state in order to cope with different runtimes of deterministic and stochastic operators. A parallelization of the variation state requires abandoning of synchronization points. Hence, the population is replaced by a pool of individuals where distributed breeder processes continuously draw samples for mating and insert offspring to replace parents. A lifetime concept is developed to keep the pool size approximately constant. A niching strategy focuses the stochastic component of the algorithm to unexplored regions

The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient-based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2, discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO, PSO with mutation, Chaotic PSO with mutation, multi-objective PSO and Quantum mechanics – based PSO algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5, presents and analyses Cuckoo search and Bat swarm algorithms and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications. The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images. The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on machine learning and deep learning.

An adaptive single-point algorithm for global numerical optimization

A real-coded multi-parent tri-hybrid evolutionary algorithm (EA) for problem optimization is presented. The hybrid EA algorithm combines the features of simplex, stochastic relaxation and multi-parent EA reproduction in a model that encourages competition among the best individual solutions front various operations. Its strength has been evaluated using standard test functions and shown to do better than other methods. The algorithm's ability to handle noise is evident when applied to experiments involving resolution of overlapping Wind Profiler data Results obtained using ram data closely matched those obtained with data preprocessed by a low pass FFT filter. Resolution of low-speed wind and clutter signals in various degrees of overlap is made possible, thereby allowing the determination of wind velocity and variance to be executed with ease.

Since coming out, novel composition test functions have received wide attention from evolutionary computation researchers and have now become the target functions for numerical optimization algorithms. However, its numerical optimization can be transformed into numerical optimization of one-dimensional functions, which significantly reduces optimization level of difficulty. A novel composition test functions algorithm for numerical optimization is proposed, which quotes a muti-population coevolutionary algorithm for numerical optimization and uses it to optimize the one-dimensional functions. The experiments proved the algorithm for numerical optimization of novel composition test functions converges to the global optimal solutions.

Towards faster convergence of evolutionary multi-criterion optimization algorithms using Karush Kuhn Tucker optimality based local search

The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks. Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima. Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues. Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. However, hybrid algorithms are also a fundamental concern in the optimization field, which aim to cumulate the advantages of different algorithms into one algorithm. In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT. HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.

This study aims to improve the performance of the Spider Wasp Optimization (SWO) algorithm, a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible with minimal cost within certain constraints. Numerous optimization algorithms have been designed in the literature and used to obtain the best solutions for specific problems. The most critical aspects in solving these problems include correctly modeling the problem, determining the problem’s parameters and constraints, and finally selecting an appropriate meta-heuristic algorithm to solve the objective function. Not every algorithm is suitable for every problem structure. Some algorithms perform better on fixed-dimension test functions, while others in solving variable-dimension test functions. In this study, the performance of the SWO algorithm was evaluated on 10 test functions previously used in the literature, consisting of three fixed-dimension functions (Schaffer, Himmelblau and Kowalik Functions) and seven variable-dimension functions, including one unimodal function (Elliptic Function) and six multimodal functions (Non-Continuous Rastrigin, Alpine, Levy, Weierstrass, Michalewicz, and Dixon & Price Functions). The solution values obtained for each of the selected functions were compared with the solutions obtained using the Harris Hawks Optimizer (HHO), the Charged System Search (CSS), and the Backtracking Search Optimization Algorithm (BSA).

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

Optimal arrangement of structural sensors in soft rock tunnels based industrial IoT applications

Swarm Intelligence (SI) and bio-inspired computation has gathered great attention in research in the last few years. Numerous SI-based optimization algorithms have gained huge popularity to solve the complex combinatorial optimization problems, non-linear design system optimization, and biometric features selection and optimization. These algorithms are inspired by nature. In biometrics, face recognition is a non-intrusive method, and facial characteristics are probably the most common biometric features to identify individuals and provide a competent level of security. This chapter presents a novel biometric feature selection algorithm based on swarm intelligence (i.e. Particle Swarm Optimization [PSO] and Bacterial Foraging Optimization Algorithm [BFOA] metaheuristics approaches). This chapter provides the stepping stone for future researchers to unveil how swarm intelligence algorithms can solve the complex optimization problems to improve the biometric identification accuracy. In addition, it can be utilized for many different areas of application.

Optimal power flow using an Improved Colliding Bodies Optimization algorithm

Purpose – Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach – The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings – A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO. Originality/value – A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

The genetic algorithm (GA) is an evolutionary optimization algorithm operating based upon reproduction, crossover and mutation. On the other hand, particle swarm optimization (PSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability based upon fuzzy rules. In this paper, particle swarm optimization in association with genetic algorithm optimization is utilized to gain the unique benefits of each optimization algorithm. Therefore, the proposed hybrid algorithm makes use of the functions and operations of both algorithms such as mutation, traditional or classical crossover, multiple-crossover and the PSO formula. Selection of these operators is based on a fuzzy probability. The performance of the hybrid algorithm in the case of solving both single-objective and multi-objective optimization problems is evaluated by utilizing challenging prominent benchmark problems including FON, ZDT1, ZDT2, ZDT3, Sphere, Schwefel 2.22, Schwefel 1.2, Rosenbrock, Noise, Step, Rastrigin, Griewank, Ackley and especially the design of the parameters of linear feedback control for a parallel-double-inverted pendulum system which is a complicated, nonlinear and unstable system. Obtained numerical results in comparison to the outcomes of other optimization algorithms in the literature demonstrate the efficiency of the hybrid of particle swarm optimization and genetic algorithm optimization with regard to addressing both single-objective and multi-objective optimization problems.