Bat Algorithm: Literature Review and Applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.Comment: 10 page

Optimal analog wavelet bases construction using hybrid optimization algorithm

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming (SQP) is proposed. CPSO is an improved PSO in which the saw tooth chaotic map is used to raise its global search ability. CPSO is a global optimizer to search the estimates of the global solution, while the SQP is employed for the local search and refining the estimates. Benefiting from good global search ability of CPSO and powerful local search ability of SQP, a high-precision global optimum in this problem can be gained. Finally, a series of optimal analog wavelet bases are constructed using the hybrid algorithm. The proposed method is tested for various wavelet bases and the improved performance is compared with previous works.Peer reviewedFinal Published versio

Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications

Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page

Swarm Intelligence and Metaphorless Algorithms for Solving Nonlinear Equation Systems

The simplicity, flexibility, and ease of implementation have motivated the use of population-based metaheuristic optimization algorithms. By focusing on two classes of such algorithms, particle swarm optimization (PSO) and the metaphorless Jaya algorithm, this thesis proposes to explore the capacity of these algorithms and their respective variants to solve difficult optimization problems, in particular systems of nonlinear equations converted into nonlinear optimization problems. For a numerical comparison to be made, the algorithms and their respective variants were implemented and tested several times in order to achieve a large sample that could be used to compare these approaches as well as find common methods that increase the effectiveness and efficiency of the algorithms. One of the approaches that was explored was dividing the solution search space into several subspaces, iteratively running an optimization algorithm on each subspace, and comparing those results to a greatly increased initial population. The insights from these previous experiments were then used to create a new hybrid approach to enhance the capabilities of the previous algorithms, which was then compared to preexisting alternatives.A simplicidade, flexibilidade e facilidade de implementa¸c˜ao motivou o uso de algoritmos metaheur´ısticos de optimiza¸c˜ao baseados em popula¸c˜oes. Focando-se em dois destes algoritmos, optimiza¸c˜ao por exame de part´ıculas (PSO) e no algoritmo Jaya, esta tese prop˜oe explorar a capacidade destes algoritmos e respectivas variantes para resolver problemas de optimiza¸c˜ao de dif´ıcil resolu¸c˜ao, em particular sistemas de equa¸c˜oes n˜ao lineares convertidos em problemas de optimiza¸c˜ao n˜ao linear. Para que fosse poss´ıvel fazer uma compara¸c˜ao num´erica, os algoritmos e respectivas variantes foram implementados e testados v´arias vezes, de modo a que fosse obtida uma amostra suficientemente grande de resultados que pudesse ser usada para comparar as diferentes abordagens, assim como encontrar m´etodos que melhorem a efic´acia e a eficiˆencia dos algoritmos. Uma das abordagens exploradas foi a divis˜ao do espa¸co de procura em v´arios subespa¸cos, iterativamente correndo um algoritmo de optimiza¸c˜ao em cada subespa¸co, e comparar esses resultados a um grande aumento da popula¸c˜ao inicial, o que melhora a qualidade da solu¸c˜ao, por´em com um custo computacional acrescido. O conhecimento resultante dessas experiˆencias foi utilizado na cria¸c˜ao de uma nova abordagem hibrida para melhorar as capacidades dos algoritmos anteriores, a qual foi comparada a alternativas pr´e-existentes

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system