Computer‐Aided Optimization of Surveillance Cameras Placement on Construction Sites

In this paper, we propose an improved ideal point setting method in the framework of MOEA/D. MOEA/D decomposes a multi-objective optimisation problem into a number of scalar optimisation problems and optimise them simultaneously. The performance of MOEA/D is highly relate to its decomposition method, and the proposed ideal point setting approach is used in the weighted Tchebycheff (TCH) and penalty-based boundary intersection (PBI) decomposition approach. It expands the region of search in the objective space by transforming the original ideal point into its symmetric point and changes the search direction of each subproblems in MOEA/D. In order to address the proposed ideal point setting method, we design a set of multi-objective problems(MOPs). The proposed method is compared with original MOEA/D-TCH and MOEA/D-PBI on MOPs. The experimental results demonstrate that our proposed ideal point setting method is very effective in terms of both diversity and convergence.

This paper presents a fast GPU-based solution to the 3D occlusion detection problem and the 3D camera placement optimisation problem. Occlusion detection is incorporated into the optimisation problem to return near-optimal positions for 3D cameras in environments containing occluding objects, which maximises the volume that is visible to the cameras. In addition, the authors' previous work on 3D sensor placement optimisation is extended to include a model for a pyramid-shaped viewing frustum and to take the camera's pose into account when computing the optimal position.

Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm

An Empirical Study on Multi-Objective Swarm Algorithm for Standard Multi-Objective Benchmark Problems

<p>Article publication in IEEE Acess uploaded here to follow EU rules mandated for projects under Horizon 2020.</p>

In this paper, we study the multiobjective co-design problem of optimal valve placement and operation in water distribution networks, addressing the minimization of average pressure and pressure variability indices. The presented formulation considers nodal pressures, pipe flows and valve locations as decision variables, where binary variables are used to model the placement of control valves. The resulting optimization problem is a multiobjective mixed integer nonlinear optimization problem. As conflicting objectives, average zone pressure and pressure variability can not be simultaneously optimized. Therefore, we present the concept of Pareto optima sets to investigate the trade-offs between the two conflicting objectives and evaluate the best compromise. We focus on the approximation of the Pareto front, the image of the Pareto optima set through the objective functions, using the weighted sum, normal boundary intersection and normalized normal constraint scalarization techniques. Each of the three methods relies on the solution of a series of single-objective optimization problems, which are mixed integer nonlinear programs (MINLPs) in our case. For the solution of each single-objective optimization problem, we implement a relaxation method that solves a sequence of nonlinear programs (NLPs) whose stationary points converge to a stationary point of the original MINLP. The relaxed NLPs have a sparse structure that come from the sparse water network graph constraints. In solving the large number of relaxed NLPs, sparsity is exploited by tailored techniques to improve the performance of the algorithms further and render the approaches scalable for large scale networks. The features of the proposed scalarization approaches are evaluated using a published benchmarking network model.

Nondominated-sorting plays an important role in multi-objective evolutionary algorithm in recent decades. However, it fails to work well when the target multi-objective problem has a complex Pareto front, brusque nondominated-sorting virtually steers by the conflicting nature of objectives, which leads to irrationality. In this paper, a novel mixed-factor evolutionary algorithm is proposed. A normalization procedure, i.e. mixed-factor, is introduced in the objective space, which links all the objectives for all the solutions of the problem to ease the conflicting nature. In the process of nondominated-sorting, the mixed factors of individual substitute the raw objectives. In order to ensure that the population are thoroughly steered through the normalized objective space, hybrid ageing operator and static hypermutation with first constructive mutation are used to guide the searching agents converge towards the true Pareto front. The algorithm proposed is operated on multi-objective knapsack problem. The effectiveness of MFEA is compared with five state-of-the-art algorithms, i.e., NSGA-II, NSGA-III, MOEA/D, SPEA2 and GrEA, in terms of five performance metrics. Simulation results demonstrate that MFEA achieves better performance.KeywordsMulti-objective evolutionary algorithmMixed-factor evolutionary algorithmMulti-objective Knapsack ProblemNormalization procedure

We have developed a system, Generalized cylinder Recognition Using Perceptual Organization (GRUPO), that performs model-based recognition of the projections of generalized cylinders. Motivated by psychological theory, the approach uses perceptual organization, the grouping of structurally significant features, to limit the object and viewpoint search spaces in recognition. The system receives feature data from a segmentation based on perceptual organization and ranks the object space according to estimates of conditional object probabilities. Depth information is not used in the approach. To complete the recognition system, several problems were solved. For modeling, theoretical contributions include a proof for the invariance of discontinuities to projection, a method to find the axis of symmetry1, and a technique for determining self-occlusion. For the recognition process, solutions to search administration, feature matching, probabilistic search of the object space, and final template matching have been developed. The theory has been implemented and tested on synthetic data.

In the recent years, the digital IIR filter design as a single objective optimization problem using evolutionary algorithms has gained much attention. In this paper, the digital IIR filter design is treated as a multiobjective problem by minimizing the magnitude response error, linear phase response error and optimal order simultaneously along with meeting the stability criterion. Hybrid gravitational search algorithm (HGSA) has been applied to design the digital IIR filter. GSA technique is hybridized with binary successive approximation (BSA) based evolutionary search method for exploring the search space locally. The relative performance of GSA and hybrid GSA has been evaluated by applying these techniques to standard mathematical test functions. The above proposed hybrid search techniques have been applied effectively to solve the multiparameter and multiobjective optimization problem of low-pass (LP), high-pass (HP), band-pass (BP), and band-stop (BS) digital IIR filter design. The obtained results reveal that the proposed technique performs better than other algorithms applied by other researchers for the design of digital IIR filter with conflicting objectives.

Resolution approach for multi-objective problems with uncertain demands

For solving multi-objective set packing problems involving constraints, this work proposes an algorithm combining an infeasible solution repair method and MOEA/D sharing the same weight vector set determining search directions in the objective space. To share the same weight vectors between repair method and evolutionary algorithm enhances the affinity of them, and the experimental results on problems with two and four objectives show that the proposed algorithm improves the search performance especially in the viewpoint of the spread of solutions in the objective space.

With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem’s characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks.

The article describes the concept of a unified information space and an algorithm of its formation using a special information and computer system. The process of incoming object search in a unified information space is considered, which makes it possible to uniquely identify it by corresponding features. One of the main tasks of a unified information space is that each information object in it is uniquely identified. For this, the identification method was used, which is based on a step-by-step analysis of object characteristics. The method of parallel information object search in unified information spaces is proposed, when information object search will be conducted independently in all unified information spaces in parallel. Experimental studies of the method of parallel information object search in unified information spaces were conducted, on the basis of which the analysis of efficiency and incoming objects search time in unified information spaces was carried out. There was experimentally approved that the more parameters that describe the information object, the less the time of object identification depends on the length of the interval. Also, there was experimentally approved that the efficiency of the searching of the incoming objects in unified information spaces tends to a directly proportional relationship with a decrease in the length of the interval and an increase in the number of parameters, and vice versa.

Multi-objective evolutionary algorithm has two goals i.e. diversity and convergence while solving MOP (Multi Objective Problem). These two goals can be achieved by proper selection of solutions. Real difficulty is selection of solution in presence of multiple conflicting objectives. MOP can be solved either by considering MOP as a whole or by using decomposition methods which solves scalar optimization sub problems simultaneously by evolving a population of solutions. This paper proposes decomposition based multi-objective genetic algorithm with Opposition operation. In this work Opposition Based Learning (OBL) concept is used in a unique way for weight vector generation. Also to have diversity among solutions and proper exploration of search space opposition based learning concept is used for population initialization and both parent and opposite parent are allowed to reproduce. The performance of the proposed methods is investigated on problems of CEC09 test suit. The experiments conducted show that OBL improves the performance of decomposition based Multi-objective Genetic Algorithm (DMOGA).

This study presents an indoor vision-based fire detection system that integrates a YOLOv8n object detection model with a Reinforcement Learning-based Optimization Algorithm (ROA) for hyperparameter tuning. The research investigates three key aspects: 1) the effectiveness of ROA in improving model performance, 2) the optimal smart camera resolution and placement for indoor fire detection, and 3) the implementation of a real-time dual-channel user notification system. The BantaySunog model iteratively adjusted a few hyperparameters using the Reinforcement Learning-based Optimization Algorithm (ROA) [Talaat & Gamel, 2023]. An episodic framework was used for training, with 15 episodes of 20 epochs each, for a maximum of 300 epochs. Each episode's top weights were carried over to the following one. To balance exploration and exploitation, ROA employed an epsilon-greedy policy with an epsilon value that decreased from 0.9 to 0.2. Experimental results show that while ROA reduced training time and yielded a more conservative prediction strategy, it did not consistently outperform the baseline YOLOv8n in terms of detection metrics such as recall and mAP50-95. Camera deployment tests identified that positioning cameras away from direct light sources significantly improved detection success, with both elevation and resolution contributing to overall system performance. Finally, a dual-channel alert mechanism combining Firebase Cloud Messaging (FCM) and Telerivet SMS API enabled the timely delivery of fire alerts, aligning with real-world standards. The findings contribute to the development of reliable and accessible fire detection systems, especially for densely populated residential areas with limited infrastructure.