The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-...

The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator...

Job Shop Scheduling is an important combinatorial optimisation problem in practice. It usually contains many (four or more) potentially conflicting objectives such as makespan and mean weighted tardiness. On the other hand, evolving dispatching rules using genetic programming has demonstrated to be a promising approach to solving job shop scheduling due to its flexibility and scalability. In th...

We consider the subset selection problem for function $f$ with constraint bound $B$ that changes over time. Within area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe adaptive variants these not able to maintain their approximation quality. Investigating recently introduced POMC Pareto optimization approach, show this algorithm effic...

Multi-Criteria Decision Making is an increasingly accepted tool for decision making in management. In this work, we highlight the application of a novel Multi-Objective Evolutionary Algorithm, NSGA-II to the risk-return trade-off for a bank loan portfolio manager. The manager of a bank operating in a competitive environment faces the standard goal of maximizing shareholder wealth. Specifically,...

This paper proposes an improved evolutionary algorithm with parallel evaluation strategy (EAPES) for solving constrained multi-objective optimization problems (CMOPs) efficiently. EAPES stores feasible solutions and infeasible solution separately in different populations, and evaluates infeasible solutions in an unusual manner, such that not only feasible solutions but also useful infeasible so...

Constrained multiobjective optimization arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. Constraint handling techniques differ in the way infeasible solutions are evolved in the evolutionary process along with their feasible counterparts. Our recently proposed threshold based penalty function gives a chance of evolution to infeas...

In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR o...