A new optimization method: Dolphin echolocation

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins and several kinds of other animals for navigation and hunting in various environments. This ability of dolphins is mimicked in this chapter to develop a new optimization method. There are different metaheuristic optimization methods, but in most of these algorithms parameter tuning takes a considerable time of the user, persuading the scientists to develop ideas to improve these methods. Studies have shown that metaheuristic algorithms have certain governing rules and knowing these rules helps to get better results. Dolphin Echolocation takes advantages of these rules and outperforms many existing optimization methods, while it has few parameters to be set. The new approach leads to excellent results with low computational efforts [1].

Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization

This paper presents a robust hybrid improved dolphin echolocation and ant colony optimization algorithm (IDEACO) for optimizing the truss structures with discrete sizing variables. The dolphin echolocation (DE) is inspired by the navigation and hunting behavior of dolphins. An improved version of dolphin echolocation (IDE), as the main engine, is proposed and uses the positive attributes of ant colony optimization (ACO) to increase the efficiency of the IDE. Here, ACO is employed to improve the precision of the global optimization solution. In the proposed hybrid optimization method, the balance between exploration and exploitation process was the main factor to control the performance of the algorithm. IDEACO algorithm performance is tested on several problems of benchmarks discrete truss structure optimization. The results indicate the excellent performance of the proposed algorithm in optimum design and rate of convergence in comparison with other metaheuristic optimization methods, so IDEACO offers a good degree of competitiveness against other existing metaheuristic methods.

Optimization is used in almost every aspect of our lives today and makes our lives easier. Optimization is generally studied as classical and heuristic optimization techniques. Classical optimization methods are not effective in real-world engineering problems. These methods, by their nature, require a mathematical model. Metaheuristic optimization methods have started to be used frequently today in the solution of these problems when a mathematical model cannot be created or a solution cannot be produced in an effective time even if it is created. These methods, by their nature, cannot produce effective results in all engineering problems. Therefore, new metaheuristic optimization methods are constantly being researched. In this study, quality test functions have been used to compare the performances of five algorithms that have been developed in recent years and produce effective results. The results obtained from these functions are shared in this study. It has been observed that the Artificial Hummingbird Optimization Algorithm (AHA) gives better results than other metaheuristic algorithms.

This study proposes a new metaheuristic optimization algorithm, namely the white-tailed eagle algorithm (WEA), for global optimization and optimum design of retaining structures. Metaheuristic optimization methods are now broadly implemented to address problems in a variety of scientific domains. These algorithms are typically inspired by the natural behavior of an agent, which can be humans, animals, plants, or any physical agent. However, a specific metaheuristic algorithm (MA) may not be able to find the optimal solution for every situation. As a result, researchers will aim to propose and discover new methods in order to identify the best solutions to a variety of problems. The white-tailed eagle algorithm (WEA) is a simple but effective nature-inspired algorithm inspired by the social life and hunting activity of white-tailed eagles. The WEA’s hunting is divided into two phases. In the first phase (exploration), white-tailed eagles seek prey inside the searching region. The eagle goes inside the designated space according to the position of the best eagle to find the optimum hunting position (exploitation). The proposed approach is tested using 13 unimodal and multimodal benchmark test functions, and the results are compared to those obtained by some well-established optimization methods. In addition, the new algorithm automates the optimum design of retaining structures under seismic load, considering two objectives: economic cost and CO2 emissions. The results of the experiments and comparisons reveal that the WEA is a high-performance algorithm that can effectively explore the decision space and outperform almost all comparative algorithms in the majority of the problems.

Randomness as source for inspiring solution search methods: Music based approaches

The performance-based optimum seismic design of steel frames is one of the most complicated and computationally demanding structural optimization problems. Metaheuristic optimization methods have been successfully used for solving engineering design problems over the last three decades. A very recently developed metaheuristic method called school-based optimization (SBO) will be utilized in the performance-based optimum seismic design of steel frames for the first time in this study. The SBO actually is an improved/enhanced version of teaching–learning-based optimization (TLBO), which mimics the teaching and learning process in a class where learners interact with the teacher and between themselves. Ad hoc strategies are adopted in order to minimize the computational cost of SBO results. The objective of the optimization problem is to minimize the weight of steel frames under interstory drift and strength constraints. Three steel frames previously designed by different metaheuristic methods including particle swarm optimization, improved quantum particle swarm optimization, firefly and modified firefly algorithms, teaching–learning-based optimization, and JAYA algorithm are used as benchmark optimization examples to verify the efficiency and robustness of the present SBO algorithm. Optimization results are compared with those of other state-of-the-art metaheuristic algorithms in terms of minimum structural weight, convergence speed, and several statistical parameters. Remarkably, in all test problems, SBO finds lighter designs with less computational effort than the TLBO and other methods available in metaheuristic optimization literature.

Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

A distinctive feature of the thematic image segmentation of onboard optoelectronic surveillance systems is the search for rational solutions in the multidimensional space of alternatives. Searching for rational solutions in a multidimensional space of alternative is peculiarized by non-linearity, non-differentiation, multi-extremality, ravine surface, lack of the analytic expression of objective functions, high computational complexity, high dimensionality of the search space, and the complex topology of the region of acceptability. Finding a solution by exact methods of optimization is complicated by non-linearity, non-differentiation and lack of the analytic expression of the objective function. High computational complexity, high dimensionality of the search space, and the complex topology of the region of acceptability lead to wasting valuable time when using precise optimization methods. Currently, methods of searching for global extremum are being developed, which provide the convergence to the exact solution of the optimization problem, provide the optimal (minimum or maximum) value of the objective function. Such methods include meta-heuristic optimization methods, which, unlike classical optimization methods, can be used even if there is no information about the nature and properties of the objective function. Meta-heuristic methods have the following features: they manage the process of finding the optimal solution; they efficiently study the search space to find the optimal solution; they use simple local search procedures and complex training processes; they are approximate methods and, as a rule, non-deterministic; they take into account the probability of trapping in a limited search space; they are universal (solve various application problems); they use a priori information to find the optimal solution. Let us detail the use of the variety of meta-heuristic optimization methods (ant colony optimization (ACO) and artificial bee colony optimization (ABC) for the thematic image segmentation of onboard optical and electronic surveillance systems.

The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.

Differential equations play a noticeable role in engineering, physics, economics, and other disciplines. In this paper, a general approach is suggested to solve a variety of linear and nonlinear ordinary differential equations (ODEs). With the aid of certain fundamental concepts of mathematics, Fourier series expansion and metaheuristic optimization methods, ODEs can be represented as an optimization problem. The aim is to minimize the weighted residual function (error function) of the ODEs. The boundary and initial values of ODEs are considered as constraints for the optimization model. Generational distance metric is used for evaluation and assessment of approximate solutions versus exact solutions. Two ODEs and one mechanical problem are approximately solved and compared with their exact solutions. The optimization task is carried out using different optimizers including the particle swarm optimization and the water cycle algorithm. The optimization results obtained show that the metaheuristic algorithms can be successfully applied for approximate solving of different types of ODEs.

Recent advancements in digitalization have resulted in the daily collection of vast amounts of data. To capitalize on this wealth of information, it is imperative to address multivariate issues, many of which are classified as NP-hard problems. One potential solution lies in metaheuristic optimization methods, which offer shorter search times and can generate approximate solutions. These techniques have seen applications across various domains. Nevertheless, a significant challenge posed by numerous representative metaheuristic methods involves the necessity for parameter configurations, the values of which notably impact convergence accuracy. This study proposes a novel optimization methodology grounded in metaheuristic optimization techniques that eliminate the need for problem-dependent accuracy affecting parameter settings. The authors assessed the efficacy of our method using standard benchmark functions and engineering benchmark problems. Furthermore, we employed it to search for multiple variables, such as historical curves, while conducting a nonlinear seismic response analysis in a real-world application scenario. Our findings confirm that our approach is not only more cost-effective but also superior in accuracy compared to previously used metaheuristic optimization methods.

The problem of system identification concerns with the design of adaptive infinite impulse response (IIR) system by determining the optimal system parameters of the unknown system on the minimization of error fitness function. The conventional system identification techniques have stability issues and problem of degradation in performance when modeled using a reduced-order system. Hence, a meta-heuristic optimization method is applied to overcome such drawbacks. In this paper, a new meta-heuristic optimization algorithm, called bat algorithm (BA), is utilized for the design of an adaptive IIR system in order to approximate the unknown system. Bat algorithm is inspired from the echolocation behavior of bats combining the advantages of existing optimization techniques. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. The proposed BA method for system identification is free from the problems encountered in conventional techniques. To valuate the performance of the proposed method, mean square error, mean square deviation and computation time are measured. Simulations have been carried out considering four benchmarked IIR systems using the same-order and reduced-order systems. The results of the proposed BA method have been compared to that of the well known optimization methods such as genetic algorithm, particle swarm optimization and cat swarm optimization. The simulation results confirm that the proposed system identification method outperforms the existing system identification methods.

Application of differential evolution-based constrained optimization methods to district energy optimization and comparison with dynamic programming

Various methods have been exploited in the blind source separation problems, especially in cocktail party problems. The most commonly used method is the independent component analysis (ICA). Many linear and nonlinear ICA methods, such as the radial basis functions (RBF) and self-organizing map (SOM) methods utilise neural networks and genetic algorithms as optimisation methods. For the contrast function, most of the traditional methods, especially the neural networks, use the gradient descent as an objective function for the ICA method. Most of these methods trap in local minima and consume numerous computation requirements. Three metaheuristic optimisation methods, namely particle, quantum particle, and glowworm swarm optimisation methods are introduced in this study to enhance the existing ICA methods. The proposed methods exhibit better results in separation than those in the traditional methods according to the following separation quality measurements: signal-to-noise ratio, signal-to-interference ratio, log-likelihood ratio, perceptual evaluation speech quality and computation time. These methods effectively achieved an independent identical distribution condition when the sampling frequency of the signals is 8 kHz.