A Competitive Game Optimization Algorithm for Solving Unmanned Aerial Vehicle Path Planning Problems

Core Concepts

A meta-heuristic optimization algorithm called competitive game optimizer (CGO) is proposed to solve the Unmanned Aerial Vehicle (UAV) path planning problem.

Abstract

The paper proposes a new meta-heuristic optimization algorithm called the competitive game optimizer (CGO) to solve the Unmanned Aerial Vehicle (UAV) path planning problem.
The CGO algorithm is inspired by the game mechanics and player behaviors in a competitive military game. It simulates the actions of players searching for supplies, engaging in battles, and moving towards the safe zone. The algorithm incorporates three key phases: exploration and exploitation, candidate replacement, and movement towards the safe zone.
In the exploration and exploitation phase, the algorithm uses Levy flight to model the players' search for supplies, with the step size adaptively changing based on the iteration number. The battle phase simulates player encounters and the resulting combat strategies. The movement towards the safe zone phase encourages players with poor objective function values to move towards the best individual.
The performance of the CGO algorithm is evaluated on a comprehensive set of 41 benchmark functions from the CEC2017 and CEC2022 test suites. Comparisons are made with 7 other widely recognized meta-heuristic optimization algorithms. The results demonstrate that the CGO algorithm achieves a good balance between exploration and exploitation, and outperforms the other algorithms on many of the test functions.
The CGO algorithm is also applied to 8 practical engineering design optimization problems and the UAV path planning problem. The simulation results show that the CGO algorithm has strong performance in dealing with these real-world optimization problems and has good application prospects.

Stats

The best, standard deviation, and mean values obtained by the 8 algorithms on the CEC2017 and CEC2022 test suites are provided in the tables.

Quotes

None.

Key Insights Distilled From

A competitive game optimization algorithm for Unmanned Aerial Vehicle path planning

by Tai-shan Lou... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09567.pdf

Deeper Inquiries

How can the CGO algorithm be further improved to enhance its performance on the more challenging composition and hybrid test functions

To enhance the performance of the CGO algorithm on more challenging composition and hybrid test functions, several improvements can be considered:

Adaptive Parameter Tuning: Implement adaptive mechanisms to adjust parameters such as step scaling factor, encounter probability, and exploration-exploitation balance dynamically during the optimization process. This adaptability can help the algorithm better navigate complex landscapes.

Dynamic Levy Flight: Modify the Levy flight strategy to adapt to the characteristics of composition and hybrid functions. Introduce variations in step sizes or distribution parameters based on the problem's structure to improve exploration efficiency.

Population Diversity Maintenance: Incorporate mechanisms to maintain population diversity, such as diversity-preserving operators or niche formation strategies. This can prevent premature convergence and help the algorithm explore diverse regions of the search space.

Hybridization with Other Algorithms: Explore the possibility of hybridizing the CGO algorithm with other meta-heuristic techniques, such as genetic algorithms or simulated annealing, to leverage their strengths and mitigate weaknesses in handling complex functions.

Problem-specific Operators: Develop problem-specific operators or heuristics tailored to the characteristics of composition and hybrid functions. These specialized components can enhance the algorithm's ability to exploit the unique features of challenging optimization landscapes.

What are the potential applications of the CGO algorithm beyond UAV path planning, and how can it be adapted to solve those problems effectively

The CGO algorithm, inspired by competitive game dynamics, can find applications beyond UAV path planning in various domains, including:

Supply Chain Optimization: CGO can be adapted to optimize supply chain logistics, considering factors like inventory management, transportation routes, and demand forecasting. The algorithm's ability to balance exploration and exploitation can improve efficiency in complex supply chain networks.

Financial Portfolio Management: Utilize CGO for portfolio optimization in finance, where the algorithm can help in asset allocation, risk management, and investment decision-making. By optimizing investment strategies, CGO can enhance portfolio performance and mitigate risks.

Smart Grid Optimization: Apply CGO to optimize energy distribution and resource allocation in smart grid systems. The algorithm can assist in load balancing, renewable energy integration, and grid stability enhancement, leading to more efficient and sustainable energy management.

Healthcare Resource Allocation: Use CGO to optimize healthcare resource allocation, such as hospital staff scheduling, medical equipment management, and patient flow optimization. By maximizing resource utilization and minimizing costs, CGO can improve healthcare service delivery.

To adapt CGO for these applications effectively, customization of algorithm parameters, operators, and termination criteria based on the specific requirements of each problem domain is essential. Additionally, incorporating domain-specific constraints and objectives into the algorithm design can enhance its performance in diverse real-world scenarios.

The CGO algorithm is inspired by player behaviors in a competitive game. Are there other types of game mechanics or natural phenomena that could inspire the development of new meta-heuristic optimization algorithms

While the CGO algorithm draws inspiration from player behaviors in competitive games, other game mechanics and natural phenomena can inspire the development of new meta-heuristic optimization algorithms. Some potential sources of inspiration include:

Swarm Intelligence: Drawing from the collective behavior of social insects like ants or bees, algorithms like Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) can be developed. These algorithms mimic the foraging and communication patterns of these insects to solve optimization problems efficiently.

Physics-based Optimization: Inspired by physical phenomena like gravitational forces or electromagnetic fields, algorithms such as Gravitational Search Algorithm (GSA) and Electromagnetic Optimization Algorithm (EOA) can be designed. These algorithms simulate the interactions between particles or charges to find optimal solutions.

Game Theory: Leveraging concepts from game theory, algorithms like Evolutionary Game Theory Optimization (EGTO) can be formulated. These algorithms model the strategic interactions between agents in a game-theoretic framework to optimize decision-making processes.

Neuroscience-inspired Optimization: Taking cues from neural networks and brain functions, algorithms like Brain Storm Optimization (BSO) or Neural Network Optimization (NNO) can be developed. These algorithms mimic neural processing and learning mechanisms to solve complex optimization problems.

By exploring diverse sources of inspiration from different disciplines, researchers can innovate and create novel meta-heuristic optimization algorithms with unique capabilities and improved performance in solving a wide range of optimization challenges.