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The 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021) was planned to be held in Krakow, Poland, however, due to COVID-19 restrictions, it is held virtually, between June 28th and July 1st 2021. IEEE CEC is the IEEE Computational Intelligence ...

Adaptive parameter control and mutation operator selection are two important research avenues in differential evolution (DE). Existing works consider the two avenues independently. In this paper, we propose to unify the two modules and develop a unified ...

The Algebraic Differential Evolution (ADE) is a recently proposed combinatorial evolutionary scheme which mimics the behaviour of the classical Differential Evolution (DE) in discrete search spaces which can be represented as finitely generated groups. ...

Differential Evolution (DE) has been successfully applied to a variety of optimization problems. The performance of DE is affected by two algorithm parameters of the scaling factor and the crossover rate. Much research has been done in order to adaptively ...

Increases in demand for a greater variety of products help companies gain more shares of growing competitive markets but, in contrast, lead to an increase in production processes and, therefore, higher costs and longer lead times. Although several ...

The performance of differential evolution (DE) algorithm depends critically on the setting of mutation factor F and crossover rate CR. In this paper, a multi-factor ranking based parameter adaptation scheme is proposed to properly set the value of F and ...

Search operator design and parameter tuning are essential parts of algorithm design. However, they often involve trial-and-error and are very time-consuming. A new differential evolution (DE) algorithm with adaptive exploration and exploitation control (...

A Micro Hydropower Plant (MHPP) is a suitable and effective mean to provide electric power to rural remote communities without harming the environment. However, the lack of resources and technical training in these communities frequently leads to designs ...

Meta-heuristic methods are commonly applied to difficult permutation type problems such as the Traveling Salesman Problem (TSP). Genetic Algorithms (GA) and Ant Colony Optimisation (ACO) are two of the most successful methods. However, a GA requires ...

One of the major challenges in optical lens design is to ascertain the lens system with the highest image quality. The image quality of the lens system, which is a measure of the performance of the lens, is a function of aberrations. This function is ...

For water environmental monitoring tasks, the use of Autonomous Surface Vehicles has been a very common option to substitute human interaction and increase the efficiency and speed in the water quality measuring process. This task requires an optimization ...

Convolutional Neural Networks (CNN) are considered the gold standard for Computer Vision Problems. However, finding the best architecture for CNN often requires handcrafted design and domain knowledge. On the other hand, Genetic Algorithms (GAs) have ...

Recent years have witnessed an increment in the number of network components communicating through many network scenarios such as multi-layer and multi-domain, and it may result in a negative impact on resource utilization. An urgent requirement arises ...

Dynamic multi-objective TSP (DMTSP) finds extensive applications in scheduling and routing problems. The task is challenging due to the change in problem environment (arrangement and number of cities) after certain time period. To solve this, in this ...

Many real-world optimization problems have dynamic multiple objectives and constrains, such problems are called dynamic multi-objective optimization problems (DMOPs). Although many dynamic multi-objective evolutionary algorithms (DMOEAs) have been ...

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world ...

Dynamic optimization problems (DOP) widely exist in many application fields and remain a challenge. The multi-population evolutionary computation approach is an efficient framework for solving the DOPs. The key issue of the multi-population approach is ...

Evolutionary algorithms are a successful application of bio-inspired behaviour in the field of Artificial Intelligence. Transferring mechanisms such as selection, mutation, and recombination, evolutionary algorithms are capable of surmounting the ...

With large wind energy penetration to power grid, power system operation has become more complex due to the intermittency of wind. For an efficient operation of wind energy, accurate wind speed forecast is in urgent need. Here, a statistical wind speed ...

Current performance indicators for assessing the diversity of many-objective optimisation approximations are often underperforming as the number of objectives increases, particularly for complex optimisation problems. In this article, a new pure unary ...

This paper proposes a competition-cooperation algorithm with bidirectional multi-population local search and local hypervolume-based strategy (CCLS) to solve multi-objective optimization problems. In the proposed method, a bidirectional multi-population ...

Existing works have reported that adaptive differential evolution algorithms, i.e., adaptive DEs, improve the MOEA/D-DE algorithm, but this result is limited to small-scale multi-objective optimization problems. This paper compares four popular adaptive ...

Combining a machine learning model within the search procedure has shown great potentials in evolutionary multiobjective optimization (EMO). The priori knowledge obtained from the property of Pareto optimal set (PS) is a great help for reproducing high-...

The utilization of stratosphere in earth observing is getting more attention. For maximizing the usage of stratosphere space, it is of vital importance to deploy the platforms of which airship is the representative, properly. Under such a background, a ...

The interactive evolutionary multi-objective optimization (IEMO) algorithms aim to learn and utilize the preference information from the decision maker (DM) during the optimization process to guide the search towards preferred solutions. In this paper, we ...

The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydro-power plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal ...

This paper investigates the suitability of several metaheuristic algorithms for the problem of compressor schedule optimization for a refrigerated warehouse. A realistic simulator of such a warehouse is used, based on domain knowledge, and tuned to match ...

In general, conventional power distribution systems lack communication and automation capabilities, making almost impossible the provision of features where self topology reconfiguration is necessary. This scenario can be changed through the emergence of ...

Wireless Rechargeable Sensor Network has emerged as a potential solution for the constrained energy problem in sensor networks in recent years. The charging process has been employed to prolong the sensor’s lifetime. An effective charging algorithm ...

The ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed ...