In an emergency situation (e.g., tsunami, chemical spill, fire) it may be necessary to displace people to safer locations. Evacuation plans must be prepared so that these movements are properly organized. Based on the ACO (ant colony optimization) meta-heuristic we design a computational model to optimize a multi-objective path-finding associated to an evacuation planning problem. The results a...

With the development of customization concept, small-batch and multi-variety production will become one major modes, especially for fast-moving consumer goods. However, this mode has two issues: high cost long manufacturing period. To address these issues, study proposes a multi-objective optimization model flexible flow-shop to optimize scheduling, which would maximize efficiency by minimizing...

The following MOEA algorithms are briefly summarized and compared: • NPGA Niched Pareto Genetic Algorithm (1994) – NPGA II (2001) • NSGA Non-dominated Sorting Genetic Algorithm (1994) – NSGA II (2000) • SPEA Strength Pareto Evolutionary Algorithm (1998) – SPEA2 (2001) – SPEA2+ (2004) – ISPEA Immunity SPEA (2003) • PAES Pareto Archived Evolution Strategy (2000) – M-PAES Mimetic PAES (2000) • PES...

In this paper the effects of elitism in the Nondominated Sorting Genetic Algorithm (NSGA) are analyzed. Three different kinds of elitism: standard, clustering and Parks & Miller techniques are investigated using two test problems. For the studied problems, the Parks & Miller mechanism generated the best results. Finally, the NSGA with Parks & Miller elitism was applied to determine the nondomin...

<span>Multi-objective algorithms are used to achieve high performance for quality of service (QoS) in wireless sensor networks (WSNs) is an important field researchers because these improve two or more conflicting objectives and present the best trade-off between solve multi-objective problems (MOPs). Previous research proposed algorithm that relies on non-dominated sorting genetic 3 (NSG...

Multiagent systems have had a powerful impact on the real world. Many of the systems it studies (air traffic, satellite coordination, rover exploration) are inherently multi-objective, but they are often treated as single-objective problems within the research. A key concept within multiagent systems is that of credit assignment: quantifying an individual agent’s impact on the overall system pe...

Coordination of operations with spatially and temporally shared resources, such as route segments, fixes, and runways, improves the efficiency of terminal airspace management. Problems in this category are, in general, computationally difficult compared to conventional scheduling problems. This paper presents a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSG...

Low specific speed centrifugal pumps (LSSCP) are widely utilized in district energy systems to promote the integration of renewable energy. However, performance LSSCP becomes inefficient due harsh operating conditions resulting substantial increase consumption. Many-objective optimization is significant improving and promoting sustainability systems. Among existing methods, global methods limit...

Multi-objective optimization (MO) is a highly demanding research topic because many realworld optimization problems consist of contradictory criteria or objectives. Considering these competing objectives concurrently, a multi-objective optimization problem (MOP) can be formulated as finding the best possible solutions that satisfy these objectives under different tradeoff situations. A family o...