Multi-Objective Optimization

Monte Carlo search algorithms rarely make the shortlist in multi-objective optimization (MOO). Traditionally, the field has belonged to evolutionary algorithms like NSGA-II and SMS-EMOA. But a paper from Paris Dauphine-PSL and Thales upends that hierarchy with an audacious twist: what if we generalized NRPA — a niche but powerful single-objective method — to handle multiple objectives, constraints, and diversity, all in one elegant framework? ...

In the paper Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments, Kirtan Rajesh and Suvidha Rupesh Kumar tackle an intricate urban challenge using AI: where to place air pollution mitigation booths across a city to optimize overall air quality under multiple, conflicting objectives1. The proposed solution uses Proximal Policy Optimization (PPO), a modern deep reinforcement learning algorithm, and a multi-dimensional reward function to model this real-world spatial optimization. But beneath the urban context lies a mathematical and algorithmic structure that holds powerful potential for business decision-making—especially where trade-offs between objectives are crucial. ...

Introduction: The Retail Map Is Not the Territory In fast-growing cities like Nairobi, Jakarta, or Lagos, deciding where to plant the next store is less about gut feeling and more about navigating an entangled network of demand, accessibility, cost, and government regulations. At Cognaptus, we developed a multi-layered AI-driven framework that not only mimics real-world logistics but also learns and forecasts future retail viability. This article explores how we combined predictive analytics, geospatial clustering, graph theory, and multi-objective optimization to determine where new retail nodes should thrive — balancing today’s needs with tomorrow’s complexities. ...