Modified dung beetle optimizer with multi-strategy for uncertain multi-modal transport path problem

A variety of real-world problems are characterized by multimodal functions, i.e., they demand the identification of several locally or globally optimal solutions. In the field of multimodal optimization, researchers actively address the development of niching techniques by inventing new methods and also by coupling existing methods with global optimization metaheuristics. In this paper, we put forward a general niching framework for the Variable Mesh Optimization (VMO) metaheuristic algorithm, coined as Variable Mesh Optimization with Niching (VMO-N). We argue that the basic VMO formulation can be easily adapted to multimodal optimization scenarios without extra adjustments. We refer to the recently published Niche-Clearing-based Variable Mesh Optimization (NC-VMO), as an instantiation of the VMO-N framework proposed here. Since the niche clearing procedure in NC-VMO requires a priori knowledge of the fitness landscape, we also introduce the Niche-based VMO via Adaptive Species Discovery (VMO-ASD) as another instance of VMO-N, in order to show its applicability to a wider range of multimodal problems. VMOASD results with some of the benchmark functions for the CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization confirm that VMO-N is a plausible general VMO niching framework for multimodal problems. We demonstrate the advantages of VMO-N over the canonical version of the algorithm, VMO-ASD over NC-VMO and also report on VMO-N's performance against other state-of-the-art niching algorithms. Further pursuits of strongly competitive versions of VMO-N will demand the use of more suitable niching methods than both clearing and ASD within the framework although this is not the focal point of this study.

Green transportation scheduling with speed control: trade-off between total transportation cost and carbon emission

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.

Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

In artificial intelligence, evolutionary algorithms (EAs) have shown to be effective and robust in solving difficult optimization problems. EAs are generic populationbased metaheuristic optimization algorithms. The mechanisms used in EAs are inspired by biological evolution: reproduction, mutation, recombination, and selection. The development of EAs can be classified into two categories: single objective and multi-objective optimization. In this thesis, both single objective and multi-objective evolutionary algorithms have been studied. For single objective optimization, various niching techniques are integrated with differential evolution (DE) and particle swarm optimization (PSO) for multi-modal optimization. Multi-modal optimization deals with optimization tasks that involve finding all or most of the global/local peaks in one single run. EAs in their original forms are usually designed for locating one single global solution. To promote and maintain formation of multiple stable subpopulations within a single population, we introduced a neighborhood mutation technique to enhance DE with ability of handling multi-modal problems. We also proposed a locally informed PSO to tackle multi-modal optimization. Beside these, several existing niching techniques from the literature were modified and improved by us.

Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

China’s transport carbon emissions are increasing quickly and the issue of emission reduction is urgent. This article aims to calculate and decompose China’s transport carbon emissions during 2001–2019. It first calculates the China’s transport carbon emissions by IPCC carbon emission factor method, and then applies the Logarithmic Mean Divisia Index (LMDI) model for decomposition analysis. The conclusion indicates that: (1) Diesel, gasoline, kerosene, and fuel oil are the major energy sources used in China’s transport sector, with the combined consumption of diesel and gasoline exceeding 70%, and the annual growth rate of energy consumption reached 8.92% during 2000–2019. Among them, natural gas and liquefied petroleum gas (LPG) have the fastest growth rate, while the only one showing a downward trend is raw coal, indicating that China’s transportation energy structure is being optimized. (2) Although China’s transport carbon emissions have been increasing, the growth rate has declined since 2013. The proportion of carbon emissions from kerosene, diesel, natural gas, and LPG increased from 2000 to 2019, while that of raw coal, gasoline, and fuel oil decreased. This suggests that the use of clean energy, air transportation, and large-scale transportation is increasing, while the use of heavily polluting fuels and small-scale road transportation is decreasing. (3) Per capita GDP is the driving factor that has the most influence on the increase of China’s transport carbon emissions. Population positively influences transportation carbon emissions too, but issues such as aging, low fertility rates, and insufficient labor force may change the direction of the impact in the next 30 years. (4) The negative effect of energy intensity on transport carbon emissions is the greatest, followed by industrial structure and energy structure. The development of highways, new energy vehicles, railway electrification, multimodal transportation, third-party logistics, and logistics information technology in China has improved the energy structure, reduced energy intensity, and brought China’s transport sector into an important stage of innovation driven and pursuit of coordinated development with the environment.

Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing

Henry gas solubility optimization: A novel physics-based algorithm

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by $$17$$17 well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence.

Using spotted hyena optimizer for training feedforward neural networks

Identifying the driving effects and differentiated characteristics of transportation carbon emissions is crucial for developing targeted and differentiated emission reduction strategies and providing a scientific basis for the Yangtze River Economic Belt. This study adopted a “top-down” approach to account for the transportation carbon emissions of the Yangtze River Economic Belt from 2000 to 2019 and constructed LMDI models and quantile regression models to estimate the driving effects and heterogeneity of influencing factors. The research results indicate the following: (1) The level of economic development is a key driving factor for transportation carbon emissions in the Yangtze River Economic Belt, with a cumulative effect of 160%. Upon inspection, the relationship between economic and transportation carbon emissions conforms to the environmental Kuznets curve. When the per capita transportation production value reaches CNY 7500, there is a “turning point” in transportation carbon emissions. (2) The population size has a driving effect on transportation carbon emissions, but as carbon emissions continue to increase, their marginal effects gradually diminish. (3) The energy structure and transportation structure have a significant inhibitory effect on transportation carbon emissions. The driving effect of the energy structure shows an “N” shape with quantile changes, while the transportation structure gradually converges. (4) Both energy intensity and transportation intensity show inhibitory effects, indicating that innovative energy substitution, optimization of transportation structure, and improvement of organizational efficiency are key ways to achieve carbon reduction in transportation. It is suggested that the Yangtze River Economic Belt should develop differentiated emission reduction paths in different regions, effectively balance economic development and carbon emission control, and promote the green and low-carbon transformation of the transportation system.