Parking lot layouts and capacity have been improved by optimizing underground parking structures and corridor designs [11], and routing strategies have been developed [12]. Research on shared mobility has examined the optimal placement of shared vehicles and their impact on urban traffic [13; 14; 15], while algorithms have been proposed to reduce the time drivers spend searching for parking [16]. Studies comparing the efficiency of marked and unmarked parking areas have evaluated the impact of different parking designs on vehicle capacity [2]. Additionally, research on parking angle optimization has aimed to determine the most suitable angles for specific parking layouts, showing that optimizing angles can significantly increase capacity [17]. In order to inform users about available parking spaces, it is first necessary to detect whether a parking spot is occupied or vacant. To achieve this, either sensor-based systems or image processing techniques are commonly employed [18; 19]. Examples of sensor-based approaches include magnetic sensors and smart cameras as used by [20],WSN (Wireless Sensor Network)-based sensors [21], optical and magnetic sensors [22], and LIDAR (Light Detection and Ranging) technology [23]. In image-based studies, various deep learning models have been utilized such as Deep Convolutional Neural Networks (DCNNs) [24], Faster Region-based Convolutional Neural Networks (Faster R-CNNs) [25], Residual Networks (ResNet50) [26], Mask Region-based Convolutional Neural Networks (Mask R-CNNs) [27], and YOLO (You Only Look Once) models [28; 29; 30]. For the optimization problem addressed in this study, a vision-based segmentation approach is necessary, similar to the study by [PERSON] et al. [31], which integrates YOLO and optimization techniques. This allows for the detection of vehicle boundaries, making it possible to perform optimization based on the remaining available space. Although many mobile applications, such as ISPARK [32] and IstPark [33], that show users available parking spots are becoming increasingly common in various cities, there are only a few examples that provide real-time solutions for unmarked areas. For unmarked parking areas, the SONAH [34] parking management system and the concept proposed by [PERSON], [PERSON], and [PERSON] [31] are rare examples. Most existing studies have either focused on detecting parking occupancy or optimizing marked parking spaces. A comprehensive heuristic optimization comparison to maximize vehicle capacity in unmarked parking areas remains lacking. [PERSON], [PERSON], and [PERSON] [31] developed the SPARK (Smart Parking Assistance and Resource Knowledge) system, which integrates YOLO v9 image segmentation with the ABC (Artificial Bee Colony) optimization algorithm to optimize vehicle placement in unmarked parking areas. However, this study did not compare different heuristic methods or consider varying vehicle sizes. This study aims to compare seven heuristic optimization algorithms--Particle Swarm Optimization (PSO), Phasor Particle Swarm Optimization (PPSO), ABC, Gray Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Multi-Population Based Differential Evolution (MDE), and the Colony-Based Search Algorithm (CSA)--to maximize the utilization of available spaces in unmarked parking areas. The performance of these algorithms will be evaluated in terms of speed and efficiency, and their impact on parking optimization for different vehicle types will be analyzed. To achieve this, three different parking scenarios are considered: (1) parallel parking optimization, (2) parking at angles between 0\({}^{\circ}\) and 45\({}^{\circ}\), and (3) free-angle parking between 0\({}^{\circ}\) and 180\({}^{\circ}\). The algorithms' performances are assessed based on computational time, space utilization, and maximum vehicle capacity.