Technology Category
Search and Optimization: Combinatorial OptimizationConstraint Satisfaction and Optimization: Constraint OptimizationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
An Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes
🤖 AI Summary
Highly constrained multi-objective optimization problems (MOOPs) with repetitive fitness landscapes—such as the multi-objective vehicle routing problem with time windows—pose significant computational challenges due to the high cost of repeatedly applying expensive multi-objective algorithms across similar instances.
Method: This paper proposes a transfer-based goal programming framework: first, a representative instance is solved using a costly multi-objective algorithm to obtain a high-quality approximate Pareto set; this set is then embedded into a goal programming model to construct three objective-specific single-objective fitness functions, which guide efficient single-objective algorithms to rapidly solve subsequent similar instances.
Contribution/Results: This work is the first to systematically exploit inter-instance fitness landscape similarity for knowledge transfer in MOOPs, balancing solution quality and computational efficiency. Experiments demonstrate that the approach significantly reduces runtime while generating high-quality compromise solutions, effectively reconciling effectiveness and efficiency.
Technology Category
Application Category
📝 Abstract
Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes.
Problem
Research questions and friction points this paper is trying to address.
Solving multiobjective problems with recurring fitness landscapes
Combining multiobjective and single-objective algorithms efficiently
Finding compromise solutions for similar problem instances
Innovation
Methods, ideas, or system contributions that make the work stand out.
Goal Programming for multiobjective problem solving
Efficient single-objective algorithms utilization
Exploiting recurring fitness landscapes similarity
🔎 Similar Papers
