Multi-modal medical image fusion brings many benefits to clinical diagnosis and analysis because it creates favorable conditions for diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are still some disadvantages to current image fusion approaches. The first one is that the fused images often have low contrast. The reason for this is several approaches use a weighted average rule for fusing low-frequency components. The second drawback is that the loss of detailed information in the fused image. This can be explained by the fact that the high-frequency components synthesized by the rules are not really effective. In this paper, two novel algorithms are proposed to tackle the above two disadvantages. The first algorithm is based on the Equilibrium optimizer algorithm (EOA) to find optimal parameters to fuse low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the sum of local energy functions using the Prewitt compass operator to create an efficient rule for the fusion of high-frequency components. This allows the fused image to significantly preserve details transferred from input images. Experimental results show that the proposed approach not only effective in significantly enhancing the quality of the fusion image but also preserving edge information carried from input images.

Multi-modal medical image fusion based on equilibrium optimizer algorithm and local energy functions

Combining Gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion

The simulation, assessment, and harvesting of maximum energy of the solar photovoltaic (PV) system require accurate and fast parameter estimation for solar cell/module models. No complete information on the PV module parameters is provided in the manufacturer’s datasheets. This leads to a nonlinear PV model with a number of unknown parameters. Recently, a new meta-heuristic algorithm called equilibrium optimizer (EO) is suggested to solve global problems. However, the EO is trapped to local optima when it is applied to real-world problems. Therefore, this paper proposes a novel and efficient algorithm called opposition-based equilibrium optimization (OBEO) for extracting the parameters of various PV models, including the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). This paper presents opposition-based learning as an update mechanism to produce the best solutions to find better search space. In this paper, the PV module parameters are extracted using three distinct points: open-circuit voltage, Voc, short-circuit current, Isc, and the point at which maximum power in the I–V curve is provided by the datasheet. The proposed OBEO algorithm minimizes the error of the I–V relationship, and the OBEO algorithm helps to find the optimal solution by generating zero error, and the search agent updates the position randomly with respect to the best solution to reach the optimal state. The proposed algorithm optimizes the parameters of the module without any assumptions. Finally, the proposed method of extracting the parameter is compared with the state-of-the-art methods to validate its performance. The proposed OBEO can achieve zero error values (fitness values) for all PV models, and the average runtime of the OBEO is 14.78 s, 28.33 s, and 32.62 s for SDM, DDM, and TDM, respectively, of all selected PV modules.

Opposition-based equilibrium optimizer algorithm for identification of equivalent circuit parameters of various photovoltaic models

The Phasmatodea population evolution algorithm (PPE) is a novel metaheuristic algorithm proposed in recent years, which simulates the evolutionary trend of stick insect population. In this article, a multigroup-based Phasmatodea population evolution algorithm with mutistrategy (MPPE) is proposed to further improve the overall performance of PPE. During the initialization period, the stick insect population is divided into multiple groups, and the step factor of the flower pollen algorithm is introduced into the population growth model of no more than half of the groups. This makes the population evolution trend diversified and prevents the algorithm from falling into the local optimal solution to a certain extent. In terms of intergroup communication, two communication strategies are adopted to mutate and replace the inferior particles, respectively, which improves the convergence speed and search ability of the algorithm. In the MPPE performance test, we compared it with PPE, five standard algorithms, and other parallel algorithms in CEC 2013 Benchmark Suite. Finally, this algorithm is applied to the IoT based electric bus scheduling for urban waterlogging situation, and the excellent performance of MPPE is verified comprehensively.

/pdf/mutigroup-based-phasmatodea-population-evolution-algorithm-2v0hmy2m.pdf

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

Capacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently proposed metaheuristic algorithm. Given the shortcomings of PPE, such as its low convergence precision, its nature to fall into local optima easily, and it being time-consuming, we propose an advanced Phasmatodea population evolution algorithm (APPE). In APPE, we delete competition, delete conditional acceptance and correspondingevolutionary trend update, and add jump mechanism, history-based searching, and population closing moving. Deleting competition and conditional acceptance and correspondingevolutionary trend update can shorten PPE running time. Adding a jump mechanism makes PPE more likely to jump out of the local optimum. Adding history-based searching and population closing moving improves PPE’s convergence accuracy. Then, we test APPE by CEC2013. We compare the proposed APPE with differential evolution (DE), sparrow search algorithm (SSA), Harris Hawk optimization (HHO), and PPE. Experiment results show that APPE has higher convergence accuracy and shorter running time. Finally, APPE also is applied to solve CVRP. From the test results of the instances, APPE is more suitable to solve CVRP.

/pdf/advanced-phasmatodea-population-evolution-algorithm-for-2y8vesu3.pdf

Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem

https://www.techscience.com/iasc/v27n1/41141/pdf

ZongLin Fu

Papers

Parallel Equilibrium Optimizer Algorithm and its application in Capacitated Vehicle Routing Problem

Software and hardware co-design and implementation of intelligent optimization algorithms