Search or ask a question

What is the speed difference between meta-heuristics and gradient-based optimization or closed-form solutions?

Minkowski addition

Null-move heuristic

Optimization problem

Collision avoidance

Best insight from top research papers

Meta-heuristics, such as those described by Stahl, are nature-inspired optimization techniques that quickly explore the search space for a global optimum. They provide the best solution by iteratively improving candidate solutions based on a fitness function. In contrast, closed-form solutions, as presented by Sun et al., are analytical methods that directly solve optimization problems using simplified linear equations. These closed-form solutions are computationally less burdensome than iterative and nonlinear minimization methods. While the speed difference between meta-heuristics and gradient-based optimization or closed-form solutions is not explicitly mentioned in the abstracts, it can be inferred that meta-heuristics may require more computational time due to their iterative nature, while closed-form solutions offer a more direct and efficient approach.

Answers from top 4 papers

PDF

Open Access

More filters

Papers (4)	Insight
Open access•Proceedings Article•DOI Closed-Form Minkowski Sum Approximations for Efficient Optimization-Based Collision Avoidance 08 Jun 2022 1 Citations	The provided paper does not mention anything about the speed difference between meta-heuristics and gradient-based optimization or closed-form solutions.
Proceedings Article•DOI Performance Comparison of Physics Based Meta-Heuristic Optimization Algorithms Doygun Demirol, Furkan Oztemiz, Ali Karci - Show less +2 more 01 Sep 2018 1 Citations	The provided paper does not provide information about the speed difference between meta-heuristics and gradient-based optimization or closed-form solutions.
Open access•Journal Article•DOI Closed-Form Solutions for Locating Heat-Concentrated Sources Using Temperature Difference Daoyuan Sun, Yifan Wu, Longjun Dong, Qiao Luo - Show less +3 more 10 Aug 2022-Mathematics 3 Citations	The provided paper does not mention the speed difference between meta-heuristics and gradient-based optimization or closed-form solutions.
Open access•Journal Article•DOI Efficiency of Meta Heuristic Optimization Algorithms Divvela Srinivasa Rao, I. M. V. Krishna - Show less +1 more 28 Nov 2022-International Journal of Advanced Research in Science, Communication and Technology	The provided paper does not mention the speed difference between meta-heuristics and gradient-based optimization or closed-form solutions.

My columns

Related Questions

What are the advantages and disadvantages of using heuristic optimization algorithms for network training?5 answersHeuristic optimization algorithms offer several advantages for network training, such as the ability to optimize complex structures like artificial neural networks (ANNs) and deep learning (DL) architectures efficiently. These algorithms, like graph neural networks (GNN) heuristics, can learn intricate patterns in combinatorial optimization problems, making them highly scalable with linear computational costs. However, some drawbacks exist, including the potential for slower convergence compared to gradient-based methods and lower reliability due to a notable number of runs failing to reach convergence. Despite these limitations, the integration of meta-heuristic (MH) algorithms with DLs is expected to enhance training processes in the future, although relevant publications in this area are currently limited.

What is Heuristics?5 answersHeuristics are mental shortcuts used to simplify decision-making processes and speed up the decision-making process. They have been studied in various fields such as economics, psychology, computer science, and mathematical optimization. Heuristics can be used consciously or subconsciously, and they can be deliberate or automatic. They are often used when classical methods fail or are not applicable, such as in situations with incomplete information, complex objective functions, or difficult constraints. Heuristics help eliminate a large subset of solution candidates using ad hoc rules, making assumptions about the problem and solution space. Although not proven or given their properties, heuristics often provide good usable solutions to otherwise unsolvable problems. Heuristics have proven to be effective in uncertain business environments and can be applied in management science for enterprises. Behavioral sciences have also shown that heuristics can be used to understand and support decision-making in the public sector.

How does heuristics used in learning algorithms?5 answersHeuristics are used in learning algorithms to improve efficiency and performance. In the context of path finding problems on graphs, heuristics are combined with search algorithms to speed up the search for target nodes. For example, in the work by Shayan Doroudi, two mastery learning heuristics are shown to be optimal policies for variants of the Bayesian knowledge tracing model. Similarly, in the work by Michal Pándy et al., a neural architecture called PHIL is presented, which uses imitation learning and graph representation learning to discover graph search and navigation heuristics. The PHIL heuristic function, trained using backpropagation, reduces the number of explored nodes compared to state-of-the-art methods and can be easily incorporated into algorithms like A*. Overall, heuristics play a crucial role in learning algorithms by providing efficient strategies for problem-solving and decision-making.

What are some mathematical examples of how heuristic methods can be used to optimize AI algorithms?2 answersHeuristic methods have been used to optimize AI algorithms in various mathematical examples. These methods involve designing procedures to solve combinatorial optimization problems. One example is the analysis and comparison of heuristic methods, where the strengths and weaknesses of newly proposed algorithms are examined using special test functions. Another example is the use of Meta-heuristics Optimization (MO) Techniques to solve real-world problems in fields such as business, logistics, and engineering. Additionally, researchers have developed heuristics that integrate learning mechanisms to improve the search process, such as recording trajectory and interpreting evolution. Furthermore, heuristic algorithms like descent local search, simulated annealing, tabu search, genetic algorithms, ant algorithms, and iterated local search have been applied to combinatorial optimization problems, such as the quadratic assignment problem.

How can heuristic methods aid in optimizing AI algorithms?5 answersHeuristic methods can aid in optimizing AI algorithms by providing efficient and effective solutions within a reasonable amount of time. These methods, such as simulated annealing, differential evolution, and genetic algorithms, operate based on principles inspired by real-world phenomena and can handle a wide range of real-world application problems. Heuristics are particularly useful in tackling combinatorial optimization problems, where finding the optimal solution is challenging. They can be used to improve the performance of neural networks by optimizing factors and calibrating the algorithm. By using heuristics, AI algorithms can be optimized without the need for prior expert knowledge and can provide high-quality solutions.

What is the cognitive heuristics?4 answersCognitive heuristics are strategies used in problem-solving that take personal experience into account. They provide guidelines and principles to consider different design possibilities during the creation of innovative products. Heuristics are efficient cognitive processes that ignore information, and they can actually improve accuracy by reducing the amount of information, computation, and time needed. In computer science and mathematical optimization, heuristics are procedures designed to find good enough solutions to optimization problems when classical methods fail or are not applicable. Heuristics often speed up computation by eliminating a large subset of solution candidates using ad hoc rules, making assumptions about the problem and solution space. Heuristics are available to help computer-based systems, such as hypertext, transcend their current limitations and better serve the user's mind.

See what other people are reading

What is the volume flow formula?

The volume flow formula is a crucial concept in various fields like mathematics and fluid dynamics. In mathematics, formulas like Prasad's formula for the covolume of $S$-arithmetic subgroups of simple connected groups are derived using advanced theories like Bruhat--Tits theory. In fluid dynamics, the Lidskii formula relates the volume and Ehrhart polynomial of flow polytopes, showing that the Ehrhart polynomial can be deduced from the volume function for these polytopes. Additionally, the transport equation for volume in flowing gases or liquids involves terms like convective/diffusive/production-type transport equations, diffusive flux density vectors, and production rates of volume, all essential for understanding the evolution of specific volume in fluids. These diverse contexts highlight the significance and complexity of volume flow formulas in different scientific disciplines.How can the epidemiological approach contribute to designing more effective health policies and targeted interventions for vulnerable populations?

The epidemiological approach plays a crucial role in designing effective health policies and targeted interventions for vulnerable populations. By identifying disease burden, causal factors, and transmission patterns, epidemiology aids in prioritizing health issues and developing interventions. Concepts like "individuals at risk," "vulnerable populations," and the life-course perspective help in selecting target populations for interventions. Furthermore, epidemiologists model epidemic dynamics to propose control strategies, including pharmaceutical and non-pharmaceutical interventions, through optimization techniques like deep reinforcement learning. This interdisciplinary collaboration between epidemiologists and optimization experts can lead to the development of more precise and impactful public health strategies, emphasizing prevention and targeted interventions for vulnerable groups. Strengthening physician education, enhancing screening uptake, and implementing stricter public health policies are also essential to limit health risks, especially in minors exposed to harmful substances like nicotine through e-cigarettes.How does the genetic algorithm differ from other optimization techniques?

The genetic algorithm (GA) stands out from other optimization techniques due to its ability to avoid local minima and efficiently solve complex problems by mimicking biological evolution processes. GA integrates with neural networks to enhance learning capabilities and input selection, making it valuable for various applications like speech processing and route optimization. Unlike deterministic algorithms, GA is non-deterministic and utilizes stochastic biomimicry, allowing it to quickly find optimal or near-optimal solutions for large optimization problems while preventing trapping in local optima. Furthermore, GA operates based on the principles of natural selection, employing sophisticated operators like selection, crossover, and mutation to search for high-quality solutions in the solution space, making it a powerful and efficient optimization tool for diverse fields.What is simulation optimization?

Simulation optimization involves utilizing simulation techniques to identify optimal input variables without exhaustively evaluating every possibility. It aims to minimize resource usage while maximizing the utilization of data obtained during the simulation process. This approach is crucial in various fields such as logistics, additive manufacturing, and complex optimization problems with stochastic elements. By simulating real-world systems and analyzing the results, simulation optimization helps in understanding physical phenomena, optimizing parameters, predicting material properties, and achieving precise manufacturing dimensions. Additionally, machine learning techniques like surrogate modeling can enhance the efficiency of simulation optimization, with modifications like using Kalman filters for robust online learning. Overall, simulation optimization plays a vital role in enhancing decision-making processes and improving system performance across different domains.How does optimal power flow analysis determine the optimal equivalent network solution for large-scale electrical systems?

Optimal Power Flow (OPF) analysis is a critical tool in determining the optimal equivalent network solution for large-scale electrical systems, addressing the challenge of minimizing generation costs, emissions, or power losses while adhering to system constraints. The Modified Ant Lion Optimization (MALO) algorithm, for instance, demonstrates the capability of swarm-based optimization techniques in solving OPF problems by minimizing cost, losses, and voltage deviation across diverse power generation sources, including thermal, wind, solar, and hydro plants. Similarly, the White Shark Optimizer (WSO) algorithm focuses on minimizing generation cost by optimizing real and reactive power in systems that integrate traditional and renewable energy sources, despite the intermittent nature of wind and solar power. The Hybrid Flying Squirrel Search Algorithm (HFSSA) further exemplifies the evolution of metaheuristic algorithms, overcoming common optimization challenges such as stagnation and premature convergence, to provide high-quality solutions for generation fuel cost, emission reduction, and transmission losses. Meanwhile, the Variable Neighborhood Descent (VND) matheuristic approach combines classical and heuristic optimization techniques to solve the OPF problem for large-scale systems, showcasing the potential of matheuristics in handling complex optimization problems. Graph Neural Networks (GNNs) trained under the imitation learning framework represent a novel approach to approximating optimal solutions for non-convex OPF problems, demonstrating scalability and efficiency in learning to compute OPF solutions for large power networks^[Context_5. The integration of deep neural networks and Lagrangian duality in the OPF-DNN model offers highly accurate and efficient approximations to the AC-OPF problem, even in large-scale power systems with thousands of buses and lines. Methods combining Affine Arithmetic (AA) and Interval Analysis (IA) address the uncertainty in OPF problems by computing outer solutions through deterministic optimization, highlighting the importance of reliable computing-based methods. The extension of Equivalent Circuit Programming to fuse optimization theory with power flow models underscores the utility of domain-specific knowledge in efficiently solving large-scale ACPF models. Lastly, the consensus-based Alternating Direction Method of Multipliers (ADMM) approach exemplifies distributed optimization techniques' role in solving large-scale OPF problems, allowing for parallel processing and independent sub-problem solving across networked local processors. Together, these advancements illustrate the multifaceted approach to determining the optimal equivalent network solution for large-scale electrical systems through OPF analysis, leveraging a combination of optimization algorithms, machine learning models, and distributed computing techniques.How does optimal power flow analysis determine the optimal equivalent network solution to reduce large-scale electrical systems?

Optimal Power Flow (OPF) analysis is a critical tool in electrical engineering, aimed at determining the most efficient operating conditions for power systems. Given the complexity and size of modern electrical networks, achieving an optimal solution in real-time presents significant computational challenges. To address these, recent research has focused on innovative methodologies to reduce the scale of these systems without significantly compromising the accuracy of the analysis. One approach to simplifying the OPF problem for large-scale networks involves the use of a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction. This method optimally balances the degree of network reduction with the resulting modeling errors, ensuring that the reduced network accurately reflects the physics of the full network. Through iterative improvements, this approach can achieve a network reduction of 25-85% with minimal voltage magnitude deviation errors, making it suitable for various power system applications. Another strategy employs graph neural networks (GNNs) to predict which lines in the network will be heavily loaded or congested, allowing for a reduced OPF (ROPF) problem that focuses only on these critical lines. This method significantly saves computing time while retaining solution quality, demonstrating the potential of machine learning models in simplifying OPF problems. Further, the application of distributed optimization techniques, such as the consensus-based Alternating Direction Method of Multipliers (ADMM), offers a way to solve large-scale OPF problems by dividing the system into partitions and solving sub-problems in parallel. This approach addresses the challenges of centralized optimization algorithms, including confidentiality concerns among different power generation companies and the computational complexity of large networks. Additionally, matheuristic approaches, combining classical and metaheuristic optimization techniques, have shown promise in solving the OPF problem for large-scale systems by minimizing fuel generation costs while adhering to the physical and operational constraints of the power system. In summary, the determination of the optimal equivalent network solution for large-scale electrical systems through OPF analysis involves a combination of network reduction methodologies, machine learning models, distributed optimization techniques, and matheuristic approaches. These strategies collectively aim to reduce computational complexity, ensure solution quality, and accommodate the operational constraints of modern power systems.What is the relationship between team composition and DISG-Type preferences?

Team composition plays a crucial role in determining DISG-Type preferences within a team. Research suggests that the composition of teams, particularly in terms of personality traits like locus of control and type-A/B behavior, significantly influences teams' preferences for changes and their magnitude. Additionally, the presence of diverse characteristics within a team, as indicated by the group faultline model, can lead to varying levels of ingroup/outgroup perceptions, affecting trust and conflict dynamics among team members. Moreover, the effectiveness of team communication, influenced by the proportion of physician FTE% relative to NPs/PAs, can impact patient outcomes and healthcare expenditures. Understanding and managing team composition based on these factors are essential for fostering a conducive environment for DISG-Type preferences and overall team performance.How to solve problems with machinery in a milling company?

To address machinery issues in a milling company, various approaches can be implemented based on the insights provided in the research contexts. Implementing tool path optimization using algorithms like the Satin Bowerbird Optimizer can significantly reduce idle times and enhance operational efficiency. Additionally, integrating acceleration sensors for condition monitoring can aid in detecting tool wear, blade breakage, and other issues, providing valuable insights into machine and tool states. Furthermore, utilizing specialized machinery like CNC machining center thread milling burrs automated processing machinery can improve efficiency by addressing problems such as low deburring efficiency and material waste. Lastly, conducting risk assessments using methods like the Fine-Kinney method can help identify and mitigate risks associated with machine tools, promoting a safer work environment in the milling sector.What are the current advancements in Bayesian neural networks for image processing in the autonomous driving domain?

Current advancements in Bayesian neural networks for image processing in autonomous driving involve improving uncertainty estimation in deep learning models. Researchers have proposed a pyramid Bayesian deep learning method to evaluate model uncertainty in semantic segmentation tasks critical for autonomous vehicles. This method optimizes Bayesian SegNet by simplifying its network structure and introducing a pyramid pooling module, enhancing performance and shortening sampling time. By utilizing Bayesian neural networks, such as the optimized Bayesian SegNet, researchers aim to enhance the accuracy and reliability of image processing tasks in autonomous driving scenarios, ultimately contributing to safer and more efficient autonomous vehicles.How do uncertainty and career choices impact the retention of individuals in STEM fields?

Uncertainty plays a crucial role in individuals' career choices and subsequent retention in STEM fields. Research indicates that uncertainty aversion significantly impacts learning, career trajectories, and decision-making. Moreover, model uncertainty affects students' choice of major, with greater uncertainty leading to a decreased likelihood of choosing certain majors, including STEM fields. Understanding the impact of demographic, math ability, and career development variables on STEM retention is essential. Studies show that initially declaring a STEM major, higher math scores, ethnic minority status, and decreased uncertainty in career thoughts predict better odds of STEM retention. Additionally, fostering stronger beliefs in engineering skills and gaining hands-on experiences can enhance career certainty among engineering students, potentially influencing their retention in STEM fields.Is the knapsack packing problem np-complete?

Yes, the knapsack packing problem is NP-complete. Various studies have demonstrated the complexity of different variants of the knapsack problem. The Positional Knapsack Problem (PKP) has been shown to be NP-hard, with its unique characteristics making it distinct from the traditional Knapsack Problem (KP). Additionally, research has highlighted that separation problems for certain types of valid inequalities for the knapsack polytope, such as extended cover inequalities, (1, k)-configuration inequalities, and weight inequalities, are all NP-complete. Even in cases where the classical continuous knapsack problem is solvable in linear time, considering capacities related to item costs can still render the problem NP-complete. Therefore, the knapsack packing problem, along with its variants and associated complexities, falls within the realm of NP-completeness.